online-coupled meteorology and chemistry models: history

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Online-coupled meteorology and chemistry models:history, current status, and outlook

Y. Zhang

To cite this version:Y. Zhang. Online-coupled meteorology and chemistry models: history, current status, and outlook.Atmospheric Chemistry and Physics, European Geosciences Union, 2008, 8 (11), pp.2895-2932. �hal-00296567�

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Atmos. Chem. Phys., 8, 2895–2932, 2008www.atmos-chem-phys.net/8/2895/2008/© Author(s) 2008. This work is distributed underthe Creative Commons Attribution 3.0 License.

AtmosphericChemistry

and Physics

Online-coupled meteorology and chemistry models: history, currentstatus, and outlook

Y. Zhang1

1North Carolina State University, Raleigh, NC 27695, USA

Received: 17 October 2007 – Published in Atmos. Chem. Phys. Discuss.: 4 February 2008Revised: 10 April 2008 – Accepted: 13 May 2008 – Published: 10 June 2008

Abstract. The climate-chemistry-aerosol-cloud-radiationfeedbacks are important processes occurring in the atmo-sphere. Accurately simulating those feedbacks requiresfully-coupled meteorology, climate, and chemistry modelsand presents significant challenges in terms of both scien-tific understanding and computational demand. This paperreviews the history and current status of the development andapplication of online-coupled meteorology and chemistrymodels, with a focus on five representative models devel-oped in the US including GATOR-GCMOM, WRF/Chem,CAM3, MIRAGE, and Caltech unified GCM. These mod-els represent the current status and/or the state-of-the sciencetreatments of online-coupled models worldwide. Their majormodel features, typical applications, and physical/chemicaltreatments are compared with a focus on model treatmentsof aerosol and cloud microphysics and aerosol-cloud inter-actions. Aerosol feedbacks to planetary boundary layer me-teorology and aerosol indirect effects are illustrated with casestudies for some of these models. Future research needs formodel development, improvement, application, as well asmajor challenges for online-coupled models are discussed.

1 Introduction

The climate-chemistry-aerosol-cloud-radiation feedbacksare important in the context of many areas including cli-mate modeling, air quality/atmospheric chemistry model-ing, numerical weather and air quality forecasting, as well

Correspondence to: Y. Zhang([email protected])

as integrated atmospheric-ocean-land surface modeling at allscales. Some potential impacts of aerosol feedbacks includea reduction of downward solar radiation (direct effect); adecrease in surface temperature and wind speed but an in-crease in relative humidity (RH) and atmospheric stability(semi-direct effect), a decrease in cloud drop size but an in-crease in drop number via serving as cloud condensation nu-clei (CCN) (first indirect effect), as well as an increase inliquid water content, cloud cover, and lifetime of low levelclouds but a suppression or enhancement of precipitation(the second indirect effect). Aerosol feedbacks are tradition-ally neglected in meteorology and air quality modeling duelargely to historical separation of meteorology, climate, andair quality communities as well as our limited understand-ing of underlying mechanisms. Those feedbacks, however,are important as models accounting (e.g., Jacobson, 2002;Chung and Seinfeld, 2005) or not accounting (e.g., Penneret al., 2003) for those feedbacks may give different results(Penner, 2003; Feichter et al., 2003; Jacobson, 2003a, b)and future climate changes may be affected by improvedair quality and vice versa through various feedback mech-anisms (Brasseur and Roeckner, 2005; Jacobson, 2002). In-creasing evidence from field measurements have shown thatsuch feedbacks ubiquitously exist among the Earth systemsincluding the atmosphere, hydrosphere, lithosphere, pedo-sphere, and biosphere. For example, a stratocumulus cloudlayer just below the advected pollutant layer observed dur-ing the 1993 North Atlantic Regional Experiment (NARE)was found to increase pollutant concentrations through theenhancement of the photolytic rates and oxidant levels (Au-diffren et al., 2004). Satellite observations have shown thatsmoke from rain forest fires in tropical areas such as Amazonand Indonesia (Kaufman and Fraser, 1997; Rosenfeld and

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2896 Y. Zhang: Online-coupled meteorology and chemistry models

Lensky, 1998; Rosenfeld, 1999) and burning of agriculturalvegetations (Warner, 1968; Rosenfeld and Woodley, 1999)can inhibit rainfall by shutting off warm rain-forming pro-cesses. This effect is due to the fact that large concentrationsof small CCN in the smoke from biomass burning lead to theformation of many small cloud droplets, thus inhibiting clouddroplet coalescence into raindrops and riming on ice precip-itation (Rosenfeld, 2000). While the suppression of rain andsnow by urban and industrial air pollution has been reported(Rosenfeld, 2000; Givati and Rosenfeld, 2004, 2005), en-hanced rainfall, on the other hand, was also found downwindof urban areas or large sources such as paper mills (Eagen etal., 1974; Jauregui and Romales, 1996) and over major ur-ban areas (Braham et al., 1981; Cerveny and Bailing, 1998),suggesting that giant CCN can enhance precipitation.

Although significant progress has been made in mod-eling climate, meteorology, air pollution in the past sev-eral decades (Seaman, 2000; Seinfeld, 2004; Seigneur,2005), several major deficiencies exist in most current globalclimate-aerosol models (e.g., Johnson et al., 1999, 2001;Mickley et al., 2004; Langner et al., 2005; Sanderson et al.,2006) that are developed either based on a general circu-lation model (GCM) or a global chemical transport model.First, the coarse spatial resolution (e.g., 4◦

×5◦) used inthose models cannot explicitly capture the fine-scale struc-ture that characterizes climatic changes (e.g., clouds, precip-itation, mesoscale circulation, sub-grid convective system,etc.). Second, the coarse time resolution (e.g., 6-h averagewind field) used in those models (except for a few modelsthat use a smaller time step, e.g., GATOR-GCMOM typ-ically updates meteorology every 5 minutes) cannot repli-cate variations at smaller scales (e.g., hourly and diurnal).Third, those models typically use simplified treatments (e.g.,simple meteorological schemes and chemistry/aerosol mi-crophysics treatments) that cannot represent intricate rela-tionships among meteorology/climate/air quality variables.Fourth, most models simulate climate and aerosols offlinewith inconsistencies in transport and no climate-chemistry-aerosol-cloud-radiation feedbacks (e.g., Prather et al., 2003;Sanderson et al., 2006). At present, most global air qual-ity models (GAQMs) are still offline. An empirical sulfate-CCN relation for aerosol indirect effects is typically usedin most GAQMs. Some feedbacks are accounted for insome global climate/chemistry models (e.g., Lohmann andFeichter, 1997; Chuang et al., 1997, 2002; Ghan et al., 2001a,b, c; Nagashima et al., 2002; Steil et al., 2003; Hauglustaineet al., 2004; Liao and Seinfeld, 2005) but either with sim-plified treatments or at a coarse resolution or both. Most airquality models at urban/regional scales, on the other hand,use offline meteorological fields without feedbacks and donot simulate aerosol direct and indirect effects (e.g., theEPA’s Community Multiple Air Quality (CMAQ) model-ing system, Byun and Ching, 1999; Binkowski and Roselle,2003). Some urban/regional air quality models are driven bya global model with inconsistent model physics (e.g., Lang-

mann et al., 2003; Hogrefe et al., 2004; Tulet et al., 2005;Sanderson et al., 2006). Most regional climate models useprescribed aerosols or simple modules without detailed gas-phase chemistry, aerosol microphysics, and aerosol-cloud in-teractions (e.g., Giorgi et al., 1993 a, b; Giorgi and Shields,1999). The aforementioned model deficiencies in accuratelyrepresenting atmospheric processes and feedbacks have ledto the largest uncertainties in current estimates of direct andindirect effects of aerosols on climate (IPCC, 2001; 2007)as well as the impact of climate on air quality. Accuratelysimulating those feedbacks requires fully-coupled models formeteorological, chemical, physical, and biological processesand presents significant challenges in terms of both scien-tific understanding and computational demand. In this work,the history and current status of development and applica-tion of online-coupled models worldwide are reviewed inSect. 2. Several representative online-coupled meteorologyand chemistry models developed in the US are used to illus-trate the current status of online-coupled models in Sect. 3.Their major model features, typical applications, and physi-cal/chemical treatments are compared with a focus on aerosoland cloud microphysics treatments and aerosol-cloud inter-actions. Simulated aerosol feedbacks to planetary boundarylayer meteorology and aerosol indirect effects are illustratedwith case studies for some of these models in Sect. 4. Majorchallenges and recommendations for future needs for the de-velopment, improvement, and application of online-coupledmodels are discussed in Sect. 5.

2 History of online-coupled climate/meteorology andair quality modeling

2.1 Concepts, history, and milestones of online-coupled models

Atmospheric chemistry or air quality and climate or me-teorology modeling were traditionally separated prior to1970’s. The three-dimensional (3-D) chemical transportmodels (CTMs) until that time were driven by either mea-sured/analyzed meteorological or chemical fields at a timeresolution of 1–6 h from a mesoscale meteorological modelon urban/regional scale or outputs at a much coarser timeresolution (e.g., 6-h or longer) from a GCM (referred to asoffline coupling). In addition to a large amount of data ex-change, this offline separation does not permit simulationsof feedbacks between air quality and climate/meteorologyand may result in an incompatible and inconsistent couplingbetween both meteorological and air quality models and aloss of important process information (e.g., cloud formationand precipitation) that occur at a time scale smaller than thatof the outputs from the offline climate/meteorology mod-els (Seaman, 2000; Grell et al., 2005; Baklanov and Kor-sholm, 2007). Such feedbacks, on the other hand, can be

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Y. Zhang: Online-coupled meteorology and chemistry models 2897

simulated in fully-coupled online models, without space andtime interpolation of meteorological fields but commonlywith higher computational costs.

Both offline and online models are actively used in cur-rent regional and global models. Offline models are fre-quently used in ensembles and operational forecasting, in-verse/adjoint modeling, and sensitivity simulations, whereasonline models are increasingly used for applications in whichthe feedbacks become important (e.g., locations with highfrequencies of clouds and large aerosol loadings), the localscale wind and circulation system change quickly, and thecoupled meteorology-air quality modeling is essential for ac-curate model simulations (e.g., real-time operational fore-casting or simulating the impact of future climate changeon air quality). Reported differences in simulation resultsfrom online and offline models can be fairly small or quitesignificant, depending on the level of complexities of themodel treatments and the simulated variables. For example,Mickley et al. (1999) found that differences in the simulatedradiative forcing of anthropogenic ozone (O3) from theirglobal chemistry-climate model operated online and offlineare within 2%. While their online radiation calculation wascarried out every 5-hr based on the O3 fields simulated bya detailed tropospheric O3-NOx-hydrocarbon chemistry anddid not account for the radiation feedbacks into the climatecalculation, their offline radiation calculation was based onthe monthly-mean O3 fields. Shindell et al. (2001) found thatthe tropospheric oxidation capacity in terms of hydroxyl rad-ical (OH) simulated by their online model is lower by∼10%than that of the same model but running offline. Jacob-son (2002) and Chung and Seinfeld (2005) reported a posi-tive forcing of fossil-fuel black carbon (BC) and organic mat-ter using their online-coupled models, whereas other modelsthat do not account for aerosol feedbacks and use a differentmixing state treatment for BC give a strong negative forc-ing (e.g., Penner et al., 2003). Nevertheless, there is an in-creasing recognition from science communities that online-coupled model systems represent the true, one atmosphereand are urgently needed, although there remain significantwork for such models to be mature and their applications arecurrently limited by computational constraints.

Regardless of the temporal and spatial scales of appli-cations, online-coupled models provide powerful platformsfor reproducing the feedbacks among multiple processes andvariables in varying degrees in one-atmosphere, dependingon the framework and degree of the coupling enabled in thosemodels. Two coupling frameworks are conventionally usedin all mesoscale and global online-coupled models: one cou-ples a meteorology model with an air quality model in whichthe two systems operate separately but exchange informationevery time step through an interface (referred to as separateonline coupling), the other integrates an air quality modelinto a meteorology model as a unified model system in whichmeteorology and air quality variables are simulated togetherin one time step without an interface between the two models

(referred to as unified online coupling). In models with a uni-fied online coupling, the equations can be solved simultane-ously with a nonlinear equation solver or the meteorologicaland air quality processes can be solved using operator split-ting; the latter is more often used at present. The main differ-ence between the two types of coupling is that the transportof meteorological and chemical variables is typically simu-lated with separate schemes in separate online models butthe same scheme in unified online models. Depending onthe objectives of the applications, the degrees of couplingand complexities in coupled atmospheric processes in thosemodels vary, ranging from a simple coupling of meteorologyand gas-phase chemistry (e.g., Rasch et al., 1995; Grell etal., 2000; Langmann, 2000) sophisticated coupling of me-teorology, chemistry, aerosol, radiation, and cloud (e.g., Ja-cobson, 1994, 2004b, 2006a; Grell et al., 2002, 2005). Whileonline-coupled models can in theory enable a full range offeedbacks among major components and processes, the de-gree of coupling in those models varies substantially fromslightly-coupled to moderately- or significantly-, or fully-coupled. In the slightly- or moderately-coupled models, onlyselected species other than water vapor (e.g., O3 or aerosols)and/or processes (e.g., transport of chemical species otherthan water vapor or gas-phase chemistry) are coupled andother processes (e.g., solar absorption of O3 and total radia-tion budget) remain decoupled. Feedbacks among processesmay or may not be accounted for. In the significantly- orfully-coupled models, major processes are coupled and a fullrange of atmospheric feedbacks are realistically simulated.At present, very few significantly- or fully-coupled onlinemodels exist. Most online models are still under develop-ment; they are slightly- or moderately-coupled with little orno feedbacks among major atmospheric processes. Depend-ing on the coupled components/processes, those online mod-els can be generally grouped into four main categories: on-line meteorology and pollutant transport; online meteorologyand pollutant transport and chemistry; online pollutant feed-backs to heating rates to drive meteorology; and online pollu-tant feedbacks to photolysis to drive photochemistry. Exam-ples of each category are given in Table 1; they represent var-ious degrees of coupled treatments for each category, varyingfrom highly-simplified to the most sophisticated one.

While a large number of online-coupled global climate-chemistry GCMs have been developed for simulating globalclimate change and air quality studies for more thanthree decades, there exist fewer coupled meteorology-(or climate-) chemistry models at urban and regional scales.This is largely due to the historic fact that mesoscale meteo-rology models and air pollution models were developed sepa-rately. The development of mesoscale coupled meteorology-chemistry models was driven by the needs for forecast-ing air quality in real-time and simulating feedbacks be-tween air quality and regional climate as well as responsesof air quality to changes in future regional climate, landuse, and biogenic emissions. Figure 1 shows chronology

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Table 1. Examples of treatments of online coupling of gas, aerosol, radiative, transport, and meteorological processes.

H69 C70,S79

C75 A75,Jo76T85,C85,M86

P84,G91,R95L00

B88 P92 J94,J95,J96,J97a,J97b

J02,J04a-d

G05 J06J07

F06 L08 Jo06

Online meteorology and pollutant transportO3 Y Y YO3 and some other gases and families Y Y Y Y YAll photochemically-active gases Y Y YSingle bulk or modal aerosol Y Y Y Y YAll discrete, size-resolved aerosol particles Y Y Y YAll chemicals within discrete, size-resolvedaerosol particles

Y Y Y

All bulk or modal or size-resolved hydrometeorparticles

Y Y Y Y

All discrete, size-resolved hydrometeor particlesand their aerosol inclusions

Y

Online meteorology and pollutant transport/chemistry/microphysicsNone YTime-dependent for O3 only Y Y Y YTime-dependent for O3 and some gases; steady-state or family chemistry for others gases

Y Y

Time-dependent for all reacting and transportedgases

Y Y Y Y Y Y Y

Time-dependent for aerosols with comprehensivedynamics treatments

Y Y Y Y Y Y Y

Online pollutant feedbacks to heating rates to drive meteorologyNo feedback Y Y YFeedback of online O3 to lookup-table heating rate YFeedback of online O3 to online parameterizedheating rate

Y Y

Feedback of a few gases to heating rates fromspectral radiative transfer

Y Y Y

Feedback of all photochemically-active gases toheating rates from spectral radiative transfer

Y Y Y

Feedback of online bulk or modal or size-resolvedaerosol to parameterized heating rate

Y Y Y Y

Feedback of all discrete size-resolved aerosols toheating rates from spectral solar and thermal-IRradiative transfer

Y Y Y

Feedback of all discrete size-resolved hydrom-eteors to heating rates from spectral solar andthermal-IR radiative transfer

Y Y

Online pollutant feedbacks to photolysis to drive photochemistryNo photolysis YPhotolysis from lookup table or fixed or a prepro-cessor model, without feedback

Y Y Y Y Y

Feedback of online O3 only to lookup-table pho-tolysis

Y Y

Feedback of a few gases to online photolysis fromspectral radiative transfer

Y Y Y

Feedback of all gases to online photolysis fromspectral radiative transfer

Y Y Y

Feedback of online bulk or modal or size-resolvedaerosol to parameterized photolysis schemes

Y Y

Feedback of all discrete size-resolved aerosols tophotolysis from spectral radiative transfer

Y Y Y

Feedback of all discrete size-resolved hydromete-ors to photolysis from spectral radiative transfer

Y Y

A75 – Atwater, M. A. (1975), B88 – Baklanov (1988), C70 – Clark J.H.E. (1970), C75 – Cunnold et al. (1975), C85 – Cess et al. (1985),F06 – Fast et al. (2006), G91 – Granier and Brasseur (1991), G05 – Grell et al. (2005), H69 – Hunt (1969), J94 – Jacobson (1994), J95 –Jacobson (1995), J96 – Jacobson et al. (1996), J97a – Jacobson (1997a), J97b – Jacobson (1997b), J02 – Jacobson (2002), J04a – Jacobson etal. (2004), J04b – Jacobson and Seinfeld (2004), J04c – Jacobson (2004a), J04d – Jacobson (2004b), J06 – Jacobson and Kaufmann (2006),J07 – Jacobson et al. (2007), Jo76 – Joseph (1976), Jo06 – Jockel et al. (2006), L00 – Langmann (2000), P84 – Penenko et al. (1984), P92 –Pitari et al. (1992), R95 – Rasch et al. (1995), S79 – Schlesinger and Mintz (1979), and T85 – Thompson (1985).



1960 20101980 200019901970

Scale

Chemistry/aerosol

Feedbacks

Global

Regional/Urban/Local

Strato. Chapman cycle

Strato. NOx/HOx cycles

Strato. Halogen chemistry

Strato. aerosol microphysics

Tropo. O3, CO, CH4

Tropo. SOx, NOx

Tropo. Inorganic aerosol chemistry

Tropo. BC and OC

Tropo. aerosol microphysics

Tropo. aerosol-cloud interactions

Global-to-urban treatments

Exposure/health effects

O3-heating rate-meteorology

O3-photolysis-photochemistry

GHGs-radiation-circulation

Aerosol-heating rate-meteorology

Aerosol-photolysis-photochemistry

SO42-direct radiation

SO42-indirect radiation

Other aerosol direct forcing

Other aerosol indirect effect

Climate-Chemistry-Carbon cycle

Atmosphere-Land-Ocean-Chemistry

3-D TransportO3 and/or other gases

Aerosols

Fig. 1. The development history in chronological order and milestones in terms of chemistry/aerosol and feedback treatments for online-coupled models.

ent history in chronological order and m

odels. and andent history in chronological order and m

odels. and indicate the timindicate the time and treatments in global and regional models, respectively.

of the development history and major milestones in termsof transport of gaseous and aerosols species, their chem-istry, and feedbacks among major atmospheric processes foronline-coupled models on all scales. The earliest attempt incoupling global climate/meteorology and chemistry can betraced back to late 1960’s, when 3-D transport of O3 and verysimple stratospheric chemistry (e.g., the Chapman reactions)were first incorporated into a GCM to simulate global O3production and transport simultaneously (e.g., Hunt, 1969;Clark, 1970). Coupled climate-chemistry GCMs developedin mid-late 1970’s included additional reactions (e.g., the ni-trogen oxides (NOx) catalytic cycle, and reactions betweenhydrogen and atomic oxygen) and accounted for the effectsof predicted O3 (but not other gases) on radiation heatingand the effect of O3’s heating on atmospheric circulation,which in turn affected the distributions of O3 (e.g., Cunnoldet al., 1975; Schlesinger and Mintz, 1979). 3-D transportof bulk aerosols and their feedbacks into radiation heatingto drive meteorology were also included in some early cou-pled GCMs (e.g., Atwater, 1975; Joseph, 1976; Covey et

al., 1984; Thompson, 1985; Cess e al., 1985; Malone et al.,1986; Ghan et al., 1988). The earliest attempt in couplingmeteorology and air pollution in local to regional scale mod-els can be traced back to early 1980s. The one-way cou-pling of 3-D transport of gases and gas-phase chemistry withmeteorology was included at meso-to-regional scales (e.g.,Marchuk, 1982; Penenko et al., 1984; Penenko and Aloyan,1985; and Bazhin et al., 1991) and local-to-meso scale (e.g.,Aloyan et al., 1982; Baklanov, 1988). In addition to theone-way coupling of transport and gas-phase chemistry, Bak-lanov (1988) also included highly-simplified aerosol treat-ments and the direct radiation feedbacks of bulk aerosols toheating/reflection and other atmospheric processes at a localscale.

Since the mid. 1980’s, a larger number of online-coupledglobal climate-chemistry models with various degrees ofcoupling to chemistry have been developed to address theAntarctic/stratospheric O3 depletion (e.g., Cariolle et al.,1986, 1990; Rose and Brasseur, 1989; Granier and Brasseur,1991; Austin and Butchart, 1992; Austin et al., 1992, 2000;



Pitari et al., 1992, 2002; Hack et al., 1993; Rasch et al., 1995;Jacobson, 1995; Eckman et al., 1996; Beagley et al., 1997;Shindell et al., 1998; Dameris et al., 1998, 2005; Takigawa etal., 1999; Rozanov et al., 2001; Nagashima et al., 2002; andSchnadt et al., 2002), tropospheric O3 and sulfur cycle (e.g.,Levy et al., 1985; Roelofs and Lelieveld, 1995; Roelofs etal., 1998; Feichter et al., 1996, 1997; de Laat et al., 1999;Mickley et al., 1999; Rasch et al., 2000; Barth et al., 2000;Shindell et al., 2001; Grenfell et al., 2001; Wong et al., 2004;and Jockel et al., 2006), both tropospheric and stratosphericchemistry (Jockel et al., 2006 and Teyssedre et al., 2007), andtropospheric aerosols, their direct radiative forcing and inter-actions with clouds (e.g., Taylor and Penner, 1994; Chuanget al., 1997, 2002; Lohmann and Feichter, 1997; Koch et al.,1999; Kiehl et al., 2000; Lohmann et al., 2000; Jacobson,2000, 2001a, 2002; Ghan et al., 2001a, b, c; Boucher andPham, 2002; Menon et al., 2002; Gong et al., 2002, 2003;Iversen and Seland, 2002; Derwent et al., 2003; Liao et al.,2003; Easter et al., 2004; Hauglustaine et al., 2004; Stier etal., 2005, 2007; and Lohmann et al., 2007). Such online-coupled models have also been expanded to study climate-carbon cycle-chemistry feedbacks in the middle atmosphere(e.g., Steil et al., 2003 and Manzini et al., 2003), and theinteractions among atmosphere, biosphere, ocean, and landsystems (referred to as earth system modeling) since late1990’s (e.g., Prinn et al., 1999; Gordan et al., 2000; Neelinand Zeng, 2000; Cox, 2001; Johnson et al., 2001; Khodriet al., 2001; Jacobson, 2004b, 2005b, 2006a; Jockel et al.,2005; Collins et al., 2006b; Chou et al., 2006; Doney et al.,2006; Jungclaus et al., 2006; and O’Connor et al., 2006). Theonline-coupled meteorology-chemistry models developed aturban/regional scales for studies of tropospheric air pollu-tants and their interactions with regional climate and me-teorology include those in North America (e.g., Jacobson,1994, 1997a, b; Mathur et al., 1998; Xiu et al., 1998; Coteet al., 1998; Grell et al., 2000, 2005; Fast et al., 2006; andKaminski, 2007), Asia (e.g., Uno et al., 2001; 2003), Aus-tralia (e.g., Manins, 2007), and Europe (e.g., Tulet et al.,1999, 2003, 2005, 2006; Langmann, 2000, 2007; Langmannet al., 2008; Wolke et al., 2003; Chenevez et al., 2004; Bak-lanov et al., 2004, 2007a, b, and references therein; Vogelet al., 2006; Vogel, 2007; Maurizi, 2007; and Korsholm etal., 2007). Some of European online models were developedthrough the European Cooperation in Science and Technol-ogy (COST) action 728 (http://www.cost728.org). Amongthese mesoscale models, the work done by Jacobson (1994,1997a, b) is the one with the highest degree in coupling.In his model, chemistry is solved for all transported gases;all chemically-active gases and size-resolved aerosol com-ponents are transported; and feedbacks of all photolyzinggases and aerosols to meteorology through heating rates andto photolysis through actinic fluxes are treated (see Table 1).Some of the mesoscale online meteorology-chemistry mod-els have been coupled with population exposure and healtheffects (e.g., Jacobson, 2007 and Baklanov et al., 2007b).

Several online-coupled regional climate-chemistry/aerosolmodels have also been developed since late 1999, with ei-ther a sulfate-like tracer (e.g., Qian and Giorgi, 1999) orhighly-simplified sulfate chemistry (e.g., Qian et al., 2001and Giorgi et al., 2002) simulated in a regional climatemodel. The coupling was enabled partially, i.e., only be-tween meteorology and tropospheric gas-phase chemistry insome regional online models (e.g., Grell et al., 2000; Taghaviet al., 2004 and Arteta et al., 2006); and significantly to fully,i.e., among more processes/components including meteorol-ogy, chemistry, aerosols, clouds, and radiation (e.g., Jacob-son, 1994, 1997a, b; Jacobson et al., 1996; Mathur et al.,1998; Grell et al., 2005; Fast et al., 2004, 2006; Zhang etal., 2005a, b; Hu and Zhang, 2006; Gustafson et al., 2007;Korsholm et al., 2007; Sofiev, 2007; and Knoth and Wolke,2007). Some online-coupled GCMs for stratospheric chem-istry have been reviewed in Austin et al. (2003) and Eyringet al. (2005); those for tropospheric chemistry have been re-viewed in Ghan et al. (2001c), Easter et al. (2004), Textoret al. (2006), and Ghan and Schwartz (2007), and those forearth system modeling have been reviewed in Friedlingsteinet al. (2006). Some of the mesoscale online-coupled mod-els have been briefly reviewed in Baklanov (1990, 2007) andBaklanov et al. (2007a).

The coupling in most global online-coupled climate-chemistry models, however, is largely incomplete; and hasbeen done only for very limited prognostic gaseous speciessuch as O3 and/or bulk sulfate aerosol or selected processessuch as transport and gas-phase chemistry (i.e., slightly-or moderately-coupling, e.g., Hunt, 1969; Atwater, 1975;Schlesinger and Mintz, 1979; Taylor and Penner, 1994).This is mainly because such a coupling typically restrictsto gas-phase or parameterized chemistry (and heterogeneouschemistry in some cases) and simple aerosol/cloud chem-istry and microphysics and often neglects the feedbacks be-tween prognostic chemical species (e.g., O3 and aerosols)and radiation (e.g., Roelofs and Lelieveld, 1995; Eckmanet al., 1996; Barth et al., 2000; Wong et al., 2004; Lamar-que et al., 2005) and between aerosols and clouds (e.g.,Liao et al., 2003; Lamarque et al., 2005). There are, how-ever, a few exceptions after mid. 1990’s when significantly-or fully-coupled systems were developed to enable a fullrange of feedbacks between meteorology/climate variablesand a myriad of gases and size-resolved aerosols (e.g., Ja-cobson, 1995, 2000; Ghan et al., 2001a, b, c). Simi-lar to global models, the feedbacks between meteorologyand chemical species are often neglected in many local-to-regional scale online models (e.g., Uno et al., 2001, 2003),and a full range of climate-chemistry-aerosol-cloud-radiationfeedbacks is treated in very few mesoscale models (e.g., Ja-cobson, 1994, 1997a, b; Grell et al., 2005).


http://www.cost728.org


2.2 History of online-coupled models developed in the US

The current status of a number of online models in Europehas been reviewed in Baklanov and Korsholm (2007) andBaklanov et al. (2007a). Most of the European online mod-els were developed in recent years, and very few of them arefully-coupled models that account for all major feedbacks.In this work, five online models on both regional and globalscales developed in the US are selected to represent thecurrent status of online-coupled models worldwide and re-viewed in details. These models include one global-through-urban model, i.e., the Gas, Aerosol, TranspOrt, Radiation,General Circulation, Mesoscale, Ocean Model (GATOR-GCMOM) (Jacobson, 2001b, 2002, 2004a, b; Jacobson etal., 2004), one mesoscale model, i.e., the Weather Researchand Forecasting/Chemistry model (WRF/Chem) (Grell et al.,2005; Fast et al., 2006), and three global models, i.e., theCommunity Atmospheric Model v. 3 (CAM3) (Collin et al.,2006a), the Model for Integrated Research on AtmosphericGlobal Exchanges version 2 (MIRAGE2); Textor et al.,2006; Ghan and Easter, 2006), and the Caltech unified GCM(Liao et al., 2003, 2004, 2006; Liao and Seinfeld, 2005).All these models predict gases, aerosols, and clouds withvarying degrees of complexities in chemical mechanismsand aerosol/cloud microphysics. While GATOR-GCMOM,WRF/Chem, and MIRAGE represent the state-of-the-scienceonline-coupled models in the world with many feedbacksaccounted for, CAM3 and Caltech unified GCM representthe current transition of 3-D models from offline to onlinein which meteorology and chemistry are coupled and feed-backs among various processes are being accounted for. Inthe following section, history and current status of the fivemodels along with other relevant models developed in theUS are reviewed.

Jacobson (1994) developed a unified fully-coupled on-line meteorology-chemistry-aerosol-radiation model on ur-ban and regional scale: a gas, aerosol, transport, and radia-tion air quality model/a mesoscale meteorological and tracerdispersion model (GATOR/MMTD, also called GATORM)(Jacobson, 1994; 1997a, b; Jacobson et al., 1996). This isthe first fully-coupled online model in the history that ac-counts for all major feedbacks among major atmosphericprocesses based on first principles (Jacobson, 2006a), sinceearly work on the coupling of meteorology and chemistrywere done in an either slightly- or somewhat incompletely-coupled fashion and the feedbacks among multiple processesin those online models were either omitted or largely simu-lated with simplified parameterizations. In an early versionof GATOR/MMTD, all meteorological and chemical pro-cesses were solved simultaneously online but with separatetransport schemes for meteorological and chemical variables.The two-way feedbacks between gases/aerosols and meteo-rology through solar and thermal-IR radiative transfer wereaccounted for. The same transport scheme was developedfor GATOR/MMTD in 1997 to solve transport of water va-

por, energy, and column pressure in MMTD and of chemicalspecies in GATOR (Jacobson, 1997c). GATOR/MMTD hasbeen applied to simulate gases and aerosols over Los An-geles (LA) Basin (Jacobson et al., 1996; Jacobson, 1997a,b), the effects of aerosols on vertical photolysis rate andtemperature profiles (Jacobson, 1998), nitrated and aromaticaerosols and nitrated aromatic gases as sources of ultravioletlight absorption (Jacobson, 1999a), the effects of soil mois-ture on temperatures, winds, and pollutant concentrations inLA (Jacobson, 1999b), and the effects of different vehiclefuels on cancer and mortality (Jacobson, 2007). The resultsfrom those model applications have been rigorously evalu-ated with available measurements.

Grell et al. (2000) developed a unified coupled on-line meteorology and chemistry model: Multiscale ClimateChemistry Model (MCCM, also called Mesoscale Model(MM5)/Chem). In this model, the Penn State Univer-sity (PSU)/the National Center for Atmospheric Research(NCAR) nonhydrostatic mesoscale model (MM5, Grell etal., 1994) was coupled online only with the gas-phase chem-ical mechanism of the Regional Acid Deposition Model,version 2 (RADM2, Chang et al., 1989; Stockwell et al.,1990). No aerosol and radiation processes were treated inMM5/Chem. MM5/Chem was applied and evaluated withseveral testbeds in the US (e.g., McKeen et al., 2003; Ederet al., 2005; Bao et al., 2005; Kang et al., 2005; Kim andStockwell, 2007). Built upon their work on MM5/Chem,Grell at al. (2002) developed a unified significantly-coupledmesoscale meteorology/chemistry/aerosol/radiation model,WRF/Chem. WRF/Chem represents the first communityonline-coupled model in the US. Different from other mod-els, a community model refers to a model that is publiclyavailable. This type of model represents synergetic modeldevelopment efforts by contributors from community andalso a major trend of development and application of cur-rent models including online-coupled models. Since its firstpublic release in 2002, WRF/Chem has attracted a num-ber of external developers and users from universities, re-search organizations, and private sectors to continuously andcollaboratively develop, improve, apply, and evaluate themodel. Although the coupling of all simulated processesin current version of WRF/Chem is not as completed asthat of GATOR/MMTD and some couplings are still par-tially completed and/or largely based on parameterizations(e.g., Fast-J photolysis algorithm does not account for thefeedbacks of all photochemically-active gases to photolysis),the degree of coupling for many atmospheric processes ismuch more significant as compared with earlier work. InWRF/Chem, transport of meteorological and chemical vari-ables is treated using the same vertical and horizontal co-ordinates and the same transport scheme with no interpola-tion in space and time. The meteorological model is basedon the NCAR’s WRF that offers options for hydrostatic andnonhydrostatic, with several dynamic cores (e.g., the Ad-vanced Research WRF with the Eulerian Mass (ARW) and



the Nonhydrostatic Mesoscale Model (NMM)), and manyoptions for physical parameterizations for applications at dif-ferent scales. The chemistry model of WRF/Chem is largelybased on MM5/Chem of Grell et al. (2000) but with an ad-ditional gas-phase mechanism: the Regional AtmosphericChemistry Mechanism (RACM) of Stockwell et al. (1997)and a new aerosol module: the Modal Aerosol DynamicsModel for Europe (MADE) (Ackermann et al., 1998) withthe secondary organic aerosol model (SORGAM) of Schellet al. (2001) (referred to as MADE/SORGAM). The pho-tolytic rates of photochemical reactions are calculated on-line using the Tropospheric Ultraviolet and Visible radiationmodel (TUV) algorithm of Madronich (1987), in which theradiative transfer model of Chang et al. (1989) is used to cal-culate actinic flux due to absorption by two gases (i.e., O2and O3), Rayleigh scattering, and scattering and absorptionby aerosols and clouds. The feedbacks of gases and aerosolsto radiation heating are simulated using atmospheric long-wave radiation schemes (e.g., the RRTM of Mlawer et al.,1997) and the shortwave radiation schemes (e.g., the MM5scheme of Dudia, 1989 and the Goddard scheme of Chou andSuarez, 1994) (Skamarock et al., 2005). RRTM is a spectral-band scheme based on the correlated-k method and uses pre-calculated tables to simulate feedbacks to longwave due towater vapor (H2O), O3, carbon dioxide (CO2), other tracegases such as nitrous oxide (N2O), methane (CH4), trichlo-rofluoromethane (CFC-11), dichlorofluoromethane (CFC-12), chlorofluorocarbon 22 (CFC-22), and carbon tetrachlo-ride (CC14), and clouds. The MM5 shortwave scheme simu-lates a simple downward integration of solar flux. It accountsfor clear-air scattering and absorption of H2O only (insteadof all photolyzing gases) using parameterizations and cloudalbedo and absorption using look-up tables. The Goddardshortwave scheme is used in a two-stream approach that ac-counts for scattered and reflected components over 11 spec-tral bands.

Two additional gas-phase mechanisms, two new aerosolmodules, and one photolytic algorithm have recently beenincorporated into the latest version of WRF/Chem (version2.2) by external developers (Fast et al., 2004, 2006; Zhanget al., 2005a, 2007; Hu and Zhang, 2006, 2007; Huang etal., 2006; Pan et al., 2008). The two new gas-phase mecha-nisms are the Carbon-Bond Mechanism version Z (CBM-Z)(Zaveri and Peters, 1999) and the 2005 version of CarbonBond mechanism (CB05) of Yarwood et al. (2005) and Sar-war et al. (2005, 2008) (both are variants of Carbon BondMechanism IV (CBM-IV) of Gery et al., 1989). The two newaerosol modules are the Model for Simulating Aerosol Inter-actions and Chemistry (MOSAIC) (Zaveri et al., 2008) andthe Model of Aerosol Dynamics, Reaction, Ionization, andDissolution (MADRID) (Zhang et al., 2004). An alternativephotolysis algorithm, the Fast-J scheme of Wild et al. (2000),has been incorporated into WRF/Chem by Fast et al. (2006).Fast-J scheme computes photolysis rates from the predictedO3, aerosol, and clouds following a Legendre expansion of

the exact scattering phase function, it however does not ac-count for the feedbacks of other radiatively absorbing gasessuch as NO2, formaldehyde (HCHO), peroxyacetyl nitrate(PAN), hydroperoxy radical (HO2), and nitric acid (HNO3)to the online calculation of photolysis. CBM-Z can use thephotolysis rates from either Fast-J or TUV. The aerosol opti-cal depth, single scattering albedo, and phase function expan-sion coefficients are calculated as a function of the refractiveindices and size distribution based on predicted aerosol massand composition.

On a global scale, a number of climate or air qualitymodels have been developed in the US in the past threedecades among which very few of them are online-coupledmodels (e.g., the NCAR Community Climate Model (CCM)(which was renamed later as Community AtmosphericModel (CAM)); the Pacific Northwest National laboratory(PNNL)’s MIRAGE; the Stanford University’s GATORG(which was later extended as a global-through-urban model,GATOR-GCMOM), and the Caltech unified GCM). Sinceits initial development as a GCM without chemistry, CCM0and CCM1 (Washington, 1982; Williamson et al., 1987), theNCAR CCM has evolved to be one of the first-generationunified online climate-chemistry models in the US follow-ing pioneer work by Hunt (1969) and Clark (1970), initiallywith gas-phase chemistry only (e.g., CCM2 (Hack et al.,1993; Rasch et al., 1995) and CCM3; Kiehl et al., 1998;Rasch et al., 2000; Barth et al., 2000) and most recentlywith additional aerosol treatments (e.g., CAM3 (Collins etal., 2004, 2006a, b; Rasch et al., 2006a, b) and CAM4(http://www.ccsm.ucar.edu) and online calculations of soildust and seat salt emissions (Mahowald et al., 2006a, b).

Jacobson (1995, 2000, 2001a) developed a unified fully-coupled Gas, Aerosol, TranspOrt, Radiation, and Generalcirculation model (GATORG). Similar to GATOR-MMTDon urban/regional scales, this is the first fully-coupled globalonline model in the history that accounts for all majorfeedbacks among major atmospheric processes based onfirst principles. While the gas-aerosol-radiation modules inGATORG are the same as those in GATORM, GATORG usesa 1994 version of the University of Los Angeles General Cir-culation Model (UCLA-GCM) (Arakawa and Lamb, 1981)to generate meteorology. GATORG was used to study globaldirect aerosol radiative forcing (Jacobson, 2000, 2001a). Ja-cobson (2001b, c) linked the regional GATORM and globalGATORG and developed the first in the history unified,nested global-through-urban scale Gas, Aerosol, Transport,Radiation, General Circulation, and Mesoscale Meteorolog-ical model, GATOR-GCMM. GATOR-GCMM is designedto treat gases, size- and composition-resolved aerosols, ra-diation, and meteorology for applications from the globalto urban (<5 km) scales and includes switches to run inglobal mode, regional mode, nested mode, and with/withoutgases, aerosols and cloud microphysics, radiation, meteo-rology, transport, deposition and sedimentation, and sur-face processes. All processes in all nested domains are


http://www.ccsm.ucar.edu


exactly the same, except for the horizontal boundary con-ditions and solutions to the momentum equation that aredifferent on global and regional scales. GATOR-GCMMaccounts for radiative feedbacks from gases, size-resolvedaerosols, liquid water and ice particles to meteorology onall scales and has been applied to study weather and tro-pospheric O3 in northern and central California and globaldirect forcing of BC (Jacobson, 2001c, d, 2002). GATOR-GCMM was extended to Gas, Aerosol, TranspOrt, Radiation,General Circulation, Mesoscale, Ocean Model (GATOR-GCMOM) in Jacobson (2004a, b, 2005b, 2006b) and Ja-cobson et al. (2004, 2006b, 2007) by the addition of a 2-Docean module with 3-D energy diffusion to the deep oceanand treatments of multiple-distribution, size-resolved cloudhydrometeors and interactions between these hydrometeorsand size- and distribution-resolved aerosols.

MIRAGE2 is an aerosol-climate model built upon theNCAR CAM2 climate model. Most of its treatments ofaerosol chemistry and physics are from its predecessor MI-RAGE1 (Ghan et al., 2001a, b, c; Easter et al., 2004)which used the same Pacific Northwest National Laboratory(PNNL) Global Chemistry Model (GChM) but a differentframework (i.e., the NCAR CCM2 climate model coupledonline with a chemical transport model). The NCAR CCM2climate model and GChM in MIRAGE1 can be run offline oronline via an interface (i.e., separate online coupling) (Ghanet al., 2001 a, b, c; Easter et al., 2004). In MIRAGE2,the gas/aerosol treatments are an integrated model imbed-ded in NCAR CAM2 (i.e., unified online coupling). As aresult, the treatment of the cloud processing of gas/aerosolsin MIRAGE2 is closer to that in CAM2, as compared to MI-RAGE1. Also, the transport/advection treatments in MI-RAGE 2 are numerically identical for water and gas/aerosolspecies. The prescribed CH4, NOx, and O3 but prognosticsteady state OH and HO2 are used in MIRAGE 1 and offlineoxidant chemistry (except for prognostic H2O2 using offlineHO2) is used in MIRAGE 2. Both MIRAGE 1 and 2 containidentical aerosol treatments. The aqueous chemistry and wetremoval are simulated online in MIRAGE 2.

Several other online-coupled global climate/aerosol mod-els with full oxidant chemistry have also been developedsince early 2000 but most of them do not include all feed-backs, in particular, aerosol indirect effects; and they are stillunder development (e.g., Liao et al., 2003). Among all 3-Dmodels that have been developed for climate and air qual-ity studies at all scales, GATOR-GCMOM, MIRAGE, andWRF/Chem represent the state-of-the-science global and re-gional coupled models worldwide; and GATOR-GCMOM(Jacobson, 2001 a, b, c, 2004 a, b) appears to be the onlymodel that represents gas, size- and composition-resolvedaerosol, cloud, and meteorological processes from the globaldown to urban scales via nesting, allowing feedbacks fromgases, aerosols, and clouds to meteorology and radiation onall scales in one model simulation.

3 Current treatments in online-coupled models

In this section, model features and treatments of majoraerosol and cloud processes for the five aforementionedonline-coupled meteorology and chemistry models devel-oped in the US are reviewed and intercompared. The reviewis presented in terms of model systems and typical applica-tions, aerosol and cloud properties, aerosol and cloud micro-physics and aerosol-cloud interactions.

3.1 Chemistry, emissions, and typical model applications

As shown in Table 2, the five models consist of a meteo-rology model (either a GCM or a mesoscale model) and achemical transport model with different levels of details ingas-phase chemistry and aerosol and cloud treatments rang-ing from the simplest one in CAM3 to the most complexone in GATOR-GCMOM. GATOR-GCMOM uses an ex-tended Carbon Bond mechanism (CBM-EX) with 247 gas-phase reactions among 115 chemical species. Its aque-ous chemical mechanism simulates 64 kinetic aqueous-phasereactions for sulfate, nitrate, organics, chlorine, oxidant,and radical chemistry and offers options for bulk or size-resolved chemistry. Its aerosol and cloud modules pro-vide comprehensive treatments for size-resolved, prognos-tic aerosol/cloud properties and processes. WRF/Chem of-fers four options for gas-phase mechanisms (i.e., RADM2,RACM, CBM-Z, and CB05) with 156–237 chemical re-actions among 52–77 chemical species and three aerosolmodules (i.e., MADE/SORGAM, MOSAIC, and MADRID).CBM-Z extends the original CBM-IV mechanism to func-tion properly at regional to global spatial scales and longertime periods than the typical urban air-shed simulations. TheCBM-Z version implemented in WRF/Chem also includesa condensed dimethylsulfide (DMS) photooxidation mecha-nism (Zaveri, 1997) to simulate the temperature-dependentformation of SO2, H2SO4, and methanesulfonic acid (MSA)in the marine environment. Compared with CBM-IV, themain changes in CB05 include updates of kinetic data (i.e.,rate coefficients) and photolysis data (i.e., absorption cross-sections and quantum yields), extended inorganic reactionset (e.g., reactions involving H2 and NO3), explicit acetalde-hyde, propionaldehyde and higher aldehydes, alkenes withinternal double bonds (internal olefins) (e.g., 2-butenes),oxygenated products and intermediates (e.g., higher organicperoxides and peroxycarboxylic acids), and lumped terpenechemistry. In the latest version of WRF/Chem (v. 2.2) re-leased in March, 2007, a generic chemical kinetic solver, theKinetic PreProcessor (KPP), has been included to facilitatethe users to incorporate any new gas-phase chemical mech-anisms into WRF/Chem. While MADE/SORGAM uses amodal approach with three lognormally-distributed modesto represent aerosol size distribution, the sectional approachwith a number of size sections (currently with 4 or 8 sec-tions, but it can be changed to any number of sections) is



Table 2. Model Systems and Typical Applications of Online Models developed in the US.

ModelSystem/Scale

MeteorologyModel

Chemical Transport Model(Main features)

Emissions TypicalApplications

ExampleReferences

GATOR-GCMOMandPredecessors(Global-through-urban)

MMTDGCMMGCMOM

Gas-phase chemistry: CBM-EX:(247 reactions, 115 species);Bulk or size-resolved aqueous-phase sulfate, nitrate,organics, chlorine, oxidant, radical chemistry (64 ki-netic reactions); size-resolved, prognostic aerosol/cloudwith complex processes

Online: allnatural gasesand particlesOffline:anthropogenicand volcanicemissions

Current/futuremet/chem/radfeedbacks;Direct/indirecteffects;AQ/health effect

Jacobson, 1994,1997a, b, 2001c,2002, 2004a, b;Jacobson et al.,2004, 2006a,2007

WRF/Chem(Mesoscale)

WRF RADM2, RACM, CBM-Z, CB05(156–237 reactions, 52–77 species);bulk aqueous-phaseRADM chemistry (MADE/SORGAM)or CMU mechanism(MOSAIC/MADRID;Three aerosol modules(MADE/SORGAM, MOSAIC, and MADRID withsize/mode-resolved,prognostic aerosol/cloud treatments

Online: biogenicand sea-saltemissionsOffline:anthropogenicemissions andother naturalemissions

Forecast/hindcast,Met/chem feedbacks;O3, PM2.5;Aerosol direct and in-direct effects

Grell et al. (2005);Fast et al. (2006);McQueen et al.(2005, 2007);Zhang et al.(2005a, b, 2007) ;Tie et al., 2007;Gustafson et al.,2007

CAM3 and Predeces-sors(Global)

CCM3/CCM2/CCM1

Sulfur chemistry (14 reactions),prescribed CH4, N2O, CFCs/MOZART4gas-phase chemistry (167 reactions, 63 species);Bulk aqueous-phase sulfate chemistry ofS(IV) (4 equilibria and 2kinetic reactions); prognostic aerosol/cloudtreatments with prescribed size distribution

Online: soil dust,sea-salt, andbiogenic emissionsOffline:anthropogenicemissions andother naturalemissions

Climate;Direct/indirecteffects;Hydrological cycle

Rasch et al.,1995, 2006;Kiehl et al., 1998;Collins et al.,2004, 2006a, b;Lamarque et al., 2005;Heald, 2007

MIRAGE2 and 1(Global)

CAM2/CCM2

Gas-phase CO-CH4-oxidant chem.(MIRAGE 1 only);Bulk aqueous-phase sulfate chemistry (6 equilibria and3 kinetic reactions); Mode-resolved simpleaerosol treatment; Prognosticaerosol/cloud treatments

Online: soil dustand sea- salt emis-sionsOffline:anthropogenicemissions andother naturalemissions

CO (MIRAGE 1only), Aerosolmass/number, Sulfurcycle; Direct/indirecteffects

Ghan et al., 2001a, b,Zhang et al., 2002;Easter et al., 2004;Textor et al., 2006;Ghan and Easter,2006

Caltech unified GCM(Global)

GISS GCMII’

Harvard tropospheric O3-NOx-hydrocarbonchemistry (305–346 reactions, 110–225 species); bulkaqueous-phase chemistry of S(IV) (5 equilibria and 3kinetic reactions);prognostic aerosol/cloud treatments withprescribed size distribution

Online: soil dustand sea- saltemissionsOffline:anthropogenicemissions andother naturalemissions

Global chemistry-aerosol interactions;aerosol directradiative forcing;the role ofheterogeneouschemistry;impact of futureclimate change on O3and aerosols

Liao et al., 2003,2004, 2006;Liao and Seinfeld,2005

used in MOSAIC and MADRID. RADM2 and RACM havebeen coupled with MADE/SORGAM and CBM-Z has beencoupled with MOSAIC and MADRID; CB05 has been cou-pled with MOSAIC and MADRID (Zhang et al., 2007a;Pan et al., 2008). While CBM-Z and MOSAIC have beenincluded in the latest released version 2.2 of WRF/Chem,CB05 and MADRID are being tested by the author’s groupand will be released in the near future. MADE/SORGAMis coupled with the bulk RADM aqueous-phase chemistrythat simulates aqueous-phase chemistry of sulfate with 5 ki-netic reactions, MOSAIC/MADRID is coupled with the bulkCarnegie Mellon University (CMU) aqueous-phase mecha-nism for chemistry of sulfate, nitrate, and oxidants that in-cludes 147 reactions among 71 species. While all threeaerosol modules provide size-resolved (in terms of either

mode or section) prognostic aerosol treatments, they differin some aspects of aerosol treatments for thermodynamicsand dynamics. All three aerosol modules simulate aerosoldirect radiative forcing, MOSAIC also simulates aerosolindirect forcing. CAM3 offers gas-phase chemistry withdifferent levels of details, a simple mechanism with pre-scribed methane (CH4), nitrous oxide (N2O), chlorofluoro-carbons (CFCs), radicals (e.g., OH, HO2 and nitrate radical(NO3), and oxidants (e.g., O3) and simulated sulfur dioxide(SO2)/dimethyl sulfide (DMS) chemistry and a more com-prehensive mechanism with 167 chemical reactions among63 species from the Model for Ozone and Related Chemi-cal Tracers version 4 (MOZART4). It simulates bulk sul-fate chemistry with dissolution equilibria of SO2, hydro-gen peroxide (H2O2), O3, and sulfurous acid (H2SO3) and



aqueous-phase kinetic reactions of dissolved sulfur com-pounds with oxidation state IV (S(IV)) with H2O2 and O3.It includes prognostic aerosol/cloud treatments but with pre-scribed size distribution for all aerosol components exceptfor dust and sea salt. MIRAGE2 uses offline oxidants forthe carbon monoxide (CO)-CH4-oxidant chemistry (exceptfor prognostic H2O2 using offline HO2) and treats the gas-phase oxidation of SO2 and DMS by OH. Its aqueous-phasechemistry includes dissolution equilibria of SO2, H2O2, O3,sulfuric acid (H2SO4), and methane sulfonic acid (MSA)and aqueous-phase kinetic reactions of S(IV) with H2O2and O3 in cloud water. It provides mode-resolved simpleaerosol treatment with prognostic aerosol/cloud propertiesand processes. Caltech unified GCM uses the Harvard tropo-spheric O3-NOx-hydrocarbon chemistry with 305–346 reac-tions among 110–225 species. Its bulk aqueous-phase chem-istry simulates aqueous-phase oxidation of S(IV) by H2O2and O3. Among the five models, it has the simplest aerosoltreatments and no treatments for aerosol-cloud interactions.

Emissions used in these models include both natural andanthropogenic emissions. Emissions of some sources andspecies are a strong function of temperature (e.g., biogenicVOC emissions from vegetation, evaporative emissions foranthropogenic VOCs), solar radiation (e.g., isoprene emis-sions), precipitation (e.g., mercury emissions from soils), andwind speed (e.g., dust emissions from soil erosion and seasalt emissions). In order to accurately simulate the effectof climate and meteorological changes on air quality in atruly integrated manner, meteorologically-dependent emis-sions should be treated online. Currently, emissions are,however, treated offline in most models and very few modelsinclude online emissions for all meteorologically-dependentspecies. In GATOR-GCMOM, emissions of all natural gasesand particles (e.g., sea spray and its chemicals, soil dustand its chemicals, lightning chemicals, pollen, spores, bac-teria, biogenic gases, soil NOx, and DMS from the ocean)are treated online and are affected by simulated meteorolog-ical conditions. The effect of meteorology on the height ofemissions from biomass-burning and volcanos is accountedfor. WRF/Chem contains online calculation of emissions ofbiogenic isoprene, monoterpenes, other biogenic VOCs, andnitrogen emissions by the soil based on the US EPA Bio-genic Emissions Inventory System (BEIS) (www.epa.gov/asmdnerl/biogen.html) and sea-salt (Grell et al., 2005; Fast etal., 2006). While MIRAGE 1 and MIRAGE 2 simulate sea-salt emissions online, dust emissions can be simulated eitheronline (e.g., Easter et al., 2004) or offline (e.g., Textor et al.,2006). Caltech unified GCM simulates the emissions of soildust and sea-salt online. CAM3 simulates the emissions ofsoil dust and sea-salt (Tie et al., 2005; Mahowald et al., 2006a, b) as well as biogenic species online based on the Modelof Emissions of Gases and Aerosols from Nature (MEGAN)of Guenther et al. (2006) that has been incorporated intoCAM3 along with the Model for Ozone and Related Chem-ical Tracers version 4 (MOZART-4) (Lamarque et al., 2005;

Heald, 2007) (http://www.essl.ucar.edu/LAR/2006/catalog/ACD/hess.htm), although offline biogenic emissions can alsobe used in some CAM3 simulations.

Those models have been developed for different applica-tions. GATOR-GCMOM has been applied for studying theeffect of BC within clouds and precipitation on global cli-mate (Jacobson, 2006b), the simulation of feedbacks amongmeteorology, chemistry and radiation on urban-to-globalscales for both current and future emission/climate scenarios,the estimates of global aerosol direct/indirect effects (e.g.,Jacobson, 2002; Jacobson et al., 2007), and the effects ofethanol versus gasoline vehicles on cancer and mortality inthe US (Jacobson, 2007). WRF/Chem and its variations weredeveloped and applied for real-time air quality forecasting(e.g., Grell et al., 2005; Kang et al., 2005; McKeen et al.,2005; 2007; Pagowski et al., 2006), although it has also beenapplied retrospectively for simulating concentrations and dis-tributions of tropospheric O3 and particles with aerodynamicdiameters less than or equal to 2.5µm (PM2.5) (e.g., Fast etal., 2004, 2006; Zhang et al., 2005a, Frost et al., 2006; Huand Zhang, 2006; Huang et al., 2006; Hu et al., 2007; Xie etal., 2007; Gustafson et al., 2007). The feedbacks betweenmeteorology and chemistry via aerosol radiation are stud-ied; aerosol indirect effect through affecting cloud formation,lifetime, and precipitation is being studied with MOSAIC(Gustafson et al., 2007; Zhang et al., 2007a, 2008a). CAM3and its predecessors were developed for global climate appli-cations to simulate global aerosol direct/indirect effects (e.g.,Kiehl et al., 2000; Collins et al., 2006a), global transport andchemistry of trace gas species (e.g., Rasch et al., 1994, 2000;Barth et al., 2000), global climate dynamic circulation (Hur-rell et al., 2006) and the global hydrological cycle (Hack etal., 2006). MIRAGE2 and its predecessors were developedto simulate global climate and aerosols. It has been appliedto simulate global transport and chemistry of CO, sulfur cy-cle, and aerosols (e.g., Easter et al., 2004) and global cloudradiative forcing (e.g., Ghan et al., 1997a, b) and aerosol di-rect/indirect effects (e.g., Ghan et al., 2001a, b, c). Theseresults have been evaluated rigorously using available gas,aerosol, and cloud measurements (Ghan et al., 2001a, b, c;Easter et al., 2004; Kinne et al., 2004, 2005). Caltech unifiedGCM has been applied to simulate global chemistry-aerosolinteractions; aerosol direct radiative forcing; the role of het-erogeneous chemistry; impact of future climate change onO3 and aerosols (Liao et al., 2003, 2004, 2006; Liao and Se-infeld, 2005).

3.2 Aerosol properties

As shown in Table 3, the treatments of aerosol proper-ties in those models are different in terms of composition,size distribution, aerosol mass/number concentrations, mix-ing state, hygroscopicity, and radiative properties. GATOR-GCMOM treats 47 species including sulfate, nitrate, am-monium, BC, OC, sea-salt, dust, water (H2O), carbonate


www.epa.gov/asmdnerl/biogen.html

www.epa.gov/asmdnerl/biogen.html

http://www.essl.ucar.edu/LAR/2006/catalog/ACD/hess.htm

http://www.essl.ucar.edu/LAR/2006/catalog/ACD/hess.htm


Table 3. Treatments of Aerosol Properties of Online Models.

Model System Composition Size Distribution Aerosol MixingState

AerosolMass/Number

AerosolHygroscopicity

Aerosol radiativeproperties

GATOR-GCMOM(Global-through-urban)

47 species (sulfate, nitrate,ammonium, BC, OC, sea-salt,dust, water, carbonate, crustalspecies such asCa2+, K+,and Mg2+)

Sectional(17–30)a:variable, multiplesize distributions

A coated core,internal/externalmixtures

Predicted/Predicted Simulatedhydrophobic-to-hydrophilicconversion forall aerosolcomponents

Simulated volume-averagerefractive indicesand opticalproperties based oncore-shell MIE the-ory

WRF/Chem(Mesoscale)

Sulfate, nitrate, ammonium, BC,OC, and water in all three aerosolmodules, sea-salt and carbonatein MOSAIC/MADRID, and men-thansulfonate in MOSAIC

Modal (3): variable(MADE/SORGAM)Sectional (8):variable(MOSAIC/MADRID)single sizedistribution

Internal Predicted/Predicted

Similar toMIRAGE2

Similar toMIRAGE2

CAM3 (Global) Sulfate, nitrate, ammonium, BC,OC, sea-salt, dust, water

Modal (4): pre-dicted dust andsea-salt, prescribedother aerosols;single size distribu-tion

External Prescribed or pre-dicted/Diagnosedfrom mass

hydrophobic andhydrophilicBC/OCwith a fixedconversion rate

Prescribed RI andoptical propertiesfor each aero. type,size, and wave-length, for externalmixtures

MIRAGE2(Global)

Sulfate, BC, OC, sea-salt, dust,water

Modal (4):variable; singlesize distribution

Externally mixedmodes withinternal mixtureswithin each mode

Predicted/Diagnosedor predicted

Simulated (volumeaveraged)with prescribedhygroscopities forOC and dust

ParameterizedRI and opticalproperties basedon wet radius andRI of each mode

Caltech unifiedGCM (Global)

Sulfate, nitrate,ammonium, BC, OC,sea-salt,dust, water,Ca2+

Sectional (11)prescribed forsea-salt;Sectional (6)prescribed formineral dust;Modal (1):prescribed sizedistribution forother aerosols;singlesize distributionfor all aerosols

BC, OC, andmineral dustexternallymixed withinternally-mixedSO2−

4 , NH+

4 ,

NO−

3 , sea-salt,and H2O;different aerosolmixing statesfor chemistry andradiative forcingcalculation

Predicted aerosolmass; aerosolnumber notincluded

Simulated BC/OCwith prescribedhygroscopicities

Simulated opticalproperties basedon Mie theorywith size- andwavelength-dependentrefractive indices

a The number in the parentheses indicates the total of aerosol size sections or modes used in typical applications of the models.

(CO2−

3 ), and crustal species (e.g., calcium (Ca2+), potas-sium (K+), and magnesium (Mg2+)) and their salts. MI-RAGE2 treats the least number of species including sul-fate, BC, organic carbon (OC), sea-salt, dust, and wa-ter (H2O). Nitrate and ammonium are treated in CAM3,WRF/Chem, and Caltech unified GCM. Additional speciessuch as calcium (Ca2+) and carbonate (CO2−

3 ) are treatedin WRF/Chem-MOSAIC/MADRID. Both CAM3 and MI-RAGE2 use modal approaches with four modes to repre-sent aerosol size distributions. GATOR-GCMOM uses a sec-tional approach with 17–30 size sections for typical applica-tions. WRF/Chem offers both approaches depending on theaerosol module selected (e.g., modal approach with 3 modesfor MADE/SORGAM and sectional approach with 8 sectionsfor MOSAIC and MADRID for typical applications). MO-SAIC and MADRID can be applied for any number of size

sections. Caltech unified GCM prescribes size distributionof sea-salt and dust with the sectional distribution but that ofother aerosols with the modal distribution. Size distributionof all aerosol components are prescribed in Caltech unifiedGCM and that of all aerosols except sea-salt and dust is pre-scribed in CAM3; they are predicted in the other three mod-els. Prescribed aerosol size distribution may introduce errorsin simulated aerosol direct and indirect radiative forcing thathighly depends on aerosol size distributions.

The mixing state of aerosols affects significantly the pre-dictions of direct/indirect radiative forcing. For exam-ple, the direct forcing of BC is 0.27 for externally-mixed(i.e., distinct from other aerosol particles), 0.78 for well-mixed (i.e., incorporated within other aerosol particles), and0.54 W m−2 for core treatments (i.e., a black-carbon corecould be surrounded by a well mixed shell), according



to Jacobson (2000). The core treatment results in val-ues of absorption/scattering coefficients and single scatter-ing albedo that are lower than those with well-mixed treat-ment but higher than those with the externally-mixed as-sumption. Most models assume aerosols to be either com-pletely externally- or internally-mixed. The internally-mixedhydrophilic treatment for BC is unphysical and reality liesbetween the externally-mixed, hydrophobic, and core treat-ments. Available measurements indicate that BC particlesare coated with a shell containing other soluble species suchas sulfate, nitrate, and ammonium (e.g., Katrlnak et al.,1992, 1993; Posfai et al., 1999). Among the five models,GATOR-GCMOM is the only model treating the transitionof externally-mixed aerosols into internally-mixed aerosolswith a coated BC core. It treats one or more size dis-tributions of aerosols. The multiple aerosol size distribu-tions represent aerosols with different sources and mixingstates (e.g., freshly-emitted BC, internally-mixed aerosols,and aerosols with a coated BC core). The other fourmodels treat a single aerosol distribution in either exter-nal or internal mixtures (e.g., external mixture in CAM3,internal mixture in WRF/Chem, externally-mixed aerosolmodes with internal mixtures of aerosol components withineach mode in MIRAGE2, and BC, OC, and mineral dustexternally-mixed with internally-mixed other aerosols inCaltech unified GCM).

All five models predict aerosol mass concentration.CAM3 can also use offline aerosol mass concentrations.Aerosol number concentration is diagnosed (e.g., CAM3)or predicted (e.g., GATOR-GCMOM, WRF/Chem) or both(e.g., MIRAGE 2), but it is not included in the Caltech unifiedGCM. It is noted that a fixed standard deviation is used forboth Aitken and accumulation modes in MADE/SORGAMin WRF/Chem, which introduces errors in simulated aerosolnumber and mass size distributions (Zhang et al., 1999). Thesimulated aerosol direct and indirect forcing depend on par-ticle size and hygroscopicity, which should be included inatmospheric models for an accurate prediction. GATOR sim-ulates hydrophobic-to-hydrophilic conversion for all aerosolcomponents, MIRAGE2, WRF/Chem, and Caltech unifiedGCM simulate this conversion but with prescribed hygro-scopicities. For example, MIRAGE assumes a hydroscop-icity of 0.14 for OC, which is one-fourth of the value for am-monium sulfate (0.51). For BC, a very small nonzero value(10−10) is assumed to avoid computational difficulties (Ghanet al., 2001a). In Caltech unified GCM, this conversion issimulated by assuming an exponential decay lifetime of 1.15days (Liao et al., 2003). CAM3 treats hydrophobic and hy-drophilic BC/OC but with a fixed conversion rate. It alsoprescribes the hygroscopicity of individual aerosol compo-nents. One difference between MIRAGE and CAM3 is thatMIRAGE treats BC and OC from boreal fires, but CAM3does not.

For aerosol radiative properties, refractive indices (RIs)vary as a function of particle size and composition forboth aerosols and cloud droplets (as well as precipita-tion). GATOR-GCMOM assumes a BC core surroundedby a shell where the RIs of the dissolved aerosol compo-nents are determined from partial molar refraction theoryand those of the remaining aerosol components are calcu-lated to be volume-averaged based on core-shell Mie the-ory. MIRAGE2, WRF/Chem, and Caltech unified GCMpredict RIs and optical properties using Mie parameteriza-tions that are function of wet surface mode radius and RIsof wet aerosol in each mode. Volume mixing is assumedfor all components, including insoluble components. Themain difference between Caltech unified GCM and both MI-RAGE2 and WRF/Chem is that Caltech unified GCM pre-scribes size distribution (e.g., a sectional distribution for sea-salt and dust and a standard gamma distribution for otheraerosols), but MIRAGE2 predicts it. Caltech unified GCMassumes that dust is externally-mixed with internal mixturesof other aerosols (which is different from the aerosol mixingstate assumption used in the aerosol thermodynamics simu-lation). In CAM3, RIs and optical properties are prescribedfor each aerosol type, size, and wavelength of the externalmixtures.

3.3 Model treatments of cloud properties

Table 4 summarizes model treatments of cloud properties, re-flecting the levels of details in cloud microphysics treatmentsfrom the simplest in Caltech unified GCM to the most sophis-ticated in GATOR-GCMOM. Hydrometeor types in cloudsin GATOR-GCMOM include size-resolved liquid, ice, grau-pel, and aerosol core components. Liquid drops are as-sumed to be spherical. Ice crystals and graupel are assumedto be non-spherical. Their non-sphericity is modeled as acollection of spheres of the same total volume-to-area ra-tio and total volume as the nonspherical particles. GATOR-GCMOM uses prognostic, multiple size distributions (typi-cally three, for liquid, ice, and graupel), each with 30 sizesections. MIRAGE2 simulates prognostically a bulk cloudcondensate that includes cloud water and cloud ice with wa-ter/ice fractions determined diagnostically, and precipitationis treated diagnostically. WRF/Chem includes several bulkmicrophysical schemes such as the Kessler scheme (Kessler1969), the Purdue Lin scheme (Lin et al., 1983; Chen andSun, 2002), and WRF Single-Moment (WSM) 6-class grau-pel scheme (Hong et al., 2006). The Purdue Lin scheme usedwith MOSAIC has 6 prognostic variables: water vapor, 2bulk cloud categories (cloud water and ice), and 3 bulk pre-cipitation categories (rain, graupel, snow/aggregates). Thecloud droplet number was added by PNNL as a prognos-tic variable in the expanded Lin scheme that is used withMOSAIC in WRF/Chem. Both MIRAGE2 and WRF/Chempredict cloud size distribution as a single, modal distribution(Barrie et al., 2001). CAM3 treats bulk liquid and ice with



Table 4. Treatments of Cloud Properties of Online Models.

ModelSystem

Hydrometeortypes inclouds

Cloud dropletsize distribution

Cloud dropletnumber

CCN/IDNcomposition

CCN/IDNspectrum

Cloud radiativeproperties


Size-resolved liquid,ice, graupel,aerosol corecomponents instratiform subgridconvective clouds

Prognostic, sectional(30), multiple sizedistributions (3)

Prognostic, size- andcomposition-dependentfrom multipleaerosol sizedistributions

All types ofaerosols treatedfor both CCN/IDN

Predicted withKohler theory;sectional(13-17);multiple sizedistributions(1-16) forboth CCN/IDN

Simulatedvolume-averagerefractive indicesand optical propertiesbased on MIEtheory and adynamic effectivemedium approxima-tion

WRF/Chem(Mesoscale)

bulk watervapor, rain, snow,cloud ice, cloud water,and graupel or asubset of them,depending onmicrophsicsschemes usedin both stratiformand subgridconvective clouds

Prognostic, modal,single size distribu-tion(MOSAIC)

Similarto MIRAGE2(MOSAIC)

Similarto MIRAGE2but sectional;CCN only

Similarto MIRAGE2but sectional,CCN only

Similarto MIRAGE2but sectional(MOSAIC)

CAM3(Global)

Bulk liquidand ice in bothstratiform andsubgrid convectiveclouds

Prognostic inmicrophysicscalculationbut prescribedin sedimentationand radiationcalculation asa functionof temperatureby phaseand location

Prescribedor prognostic(similar toMIRAGE2)

All treatedspecies excepthydrophobic species;CCN only

Prescribed;CCN only

Similar toMIRAGE2

MIRAGE2(Global)

Bulk liquidand ice in bothstratiform and subgridconvective clouds

Prognostic, modal,single size distribu-tion

Prognostic, aerosolsize- andcomposition-dependent,parameterized

All treated species;CCN only

Function ofaerosol size andhygroscopicitybased on Kohlertheory;CCN only

Prognostic,parameterizedin terms of cloudwater, ice mass,and number

Caltechunified GCM(Global)

Bulk liquidand ice inboth stratiformand subgridconvective clouds

Diagnosed frompredicted cloudwater content;single sizedistribution

constant clouddroplet numberbased onobservations

None None Simulated basedon MIE theorywith differentparameterizationsfor liquid andice clouds

the same prognostic droplet size treatment as MIRAGE2 inmicrophysics calculation, but the droplet size treatment isprescribed in sedimentation and radiation calculation as afunction of temperature by phase and location (Boville etal., 2006). All five models distinguish large-scale strati-form and subgrid convective clouds but with some differ-ences in their treatments. For example, in GATOR-GCMOMand MIRAGE2, large-scale stratiform clouds can cover afraction of a grid cell. In WRF/Chem, stratiform cloudshave a cloud fraction of 0 or 1 and the aerosols are not af-fected by sub-grid convective clouds (e.g., Kain-Fritsch op-tion). Neglecting sub-grid cloud treatments may introducelarge errors for the horizontal grid resolution greater than 15-km. For both resolved and convective clouds in GATOR-GCMOM, microphysics is explicit and involves growth of

water vapor onto discrete size-resolved aerosol particles toform discrete, size-resolved clouds and precipitation (liquid,ice, and graupel), and aerosol inclusions are tracked in eachsize of each hydrometeor distribution (Jacobson and Kauf-man, 2006), whereas other models do not contain such de-tailed treatments.

Droplet size distribution in both models has a prescribeddispersion so that liquid water content is proportional tonumber times effective radius cubed. Caltech unified GCMtreats bulk liquid and ice with their distributions diagnosedfrom predicted cloud water content. Among the five mod-els, Caltech unified GCM is the only model that prescribescloud droplet number, which is predicted in all other fourmodels, although the prescribed cloud droplet number canalso be used in the cloud miscrophysics parameterization of



Rasch and Kristjansson (1998) in CAM3. Caltech unifiedGCM assumes a cloud droplet number of 60 and 170 cm−3,respectively, for liquid phase clouds over ocean and land,and 0.06 cm−3 for all ice clouds based on observations (DelGenio et al., 1996). CAM3, MIRAGE2, and WRF/Chemuse similar treatments for droplet number, with droplet nu-cleation parameterized by Abdul-Razzak and Ghan (2000).WRF/Chem-MOSAIC diagnoses the total number activatedfrom the sectional size distribution of the CCN, which is thenused to predict the droplet number that has a modal distri-bution. GATOR treats prognostic, size- and composition-dependent cloud droplet number from multiple aerosol sizedistributions. While an empirical relationship between sul-fate aerosols and CCN is commonly used in most atmo-spheric models, CCN is calculated from Kohler theory usingthe aerosol size distribution and hygroscopicity in all mod-els but Caltech unified GCM. MIRAGE 2 and WRF/Chemtreat the same CCN composition, except with different sizerepresentations. Other than Caltech unified GCM that doesnot treat CCN and Ice Deposition Nuclei (IDN), all otherfour models treat the competition among different aerosolspecies but the hydrophobic species are not activated inCAM3 since it assumes external-mixture. Among the fivemodels, GATOR-GCMOM is the only model that simulatescomposition of IDN. For CCN spectrum, MIRAGE 2 andWRF/Chem simulate it as a function of aerosol size and hy-groscopicity based on Kohler theory. CAM3 uses prescribedCCN spectrum. GATOR predicts spectra of both CCN andIDN with 13–17 sections and 1–16 size distributions for typ-ical applications. MIRAGE 2 and CAM3 use a prognosticparameterization in terms of cloud water and ice mass andnumber to predict cloud radiative properties. WRF/Chemalso uses the same method but with sectional approach. Cal-tech unified GCM simulates cloud optical properties basedon MIE theory and prescribed Gamma distributions for liquidclouds and phase functions of Mishchenko et al. (1996) (Liaoet al., 2003). GATOR-GCMOM simulates volume-averagecloud refractive indices (RIs) and optical properties based onMIE theory and an iterative dynamic effective medium ap-proximation (IDEMA) to account for multiple BC inclusionswithin clouds. The IDEMA is superior to classic effective-medium approximation that is used by several mixing rulessuch as the volume-average RI mixing rule, the volume av-erage dielectric constant mixing rule, the Maxwell-Garnettmixing rule, and the Bruggeman mixing rule in two aspects(Jacobson, 2006a). First, the IDEMA accounts for polydis-persion of spherical absorbing inclusions within the mediumand gives different efficiencies at a given wavelength for agiven volume fraction but with different size distributions ofabsorbing material, as occurs in reality. Second, the IDEMAalso accounts for light interactions as a function of size of thematerial included.

3.4 Aerosol thermodynamics and dynamics

Table 5 shows model treatments of aerosol chemistry andmicrophysics that differ in many aspects. Caltech uni-fied GCM treats aerosol thermodynamics only, the rest ofmodels treat both aerosol thermodynamics and dynamicssuch as coagulation and new particle formation via homoge-neous nucleation. It uses a thermodynamic module, ISOR-ROPIA (“equilibrium” in Greek) of Nenes et al. (1998),for inorganic aerosols with regime equilibrium among sul-fate, nitrate, ammonium, sea-salt, and water. Similar tomany global models, MIRAGE2 does not treat nitrate; itsimulates a simple inorganic aerosol equilibrium involv-ing ammonium sulfate ((NH4)2SO4) and precursor gases.MOZART4 aerosol module in CAM3 uses regime equilib-rium for sulfate, ammonium, and nitrate that accounts forcases with sulfate neutral, rich, and very rich. GATOR-GCMOM uses the EQUIlibrium SOLVer Version 2 (EQUI-SOLV II) of Jacobson (1999c) that simulates equilibria ofall major inorganic salts and crustal species and that pro-vides the most comprehensive treatments among inorganicaerosol thermodynamic modules used in 3-D models (Zhanget al., 2000). EQUISOLV II has been extended to the Pre-dictor of Nonequilibrium Growth (PNG)-EQUISOLV II toovercome the oscillatory problem in solving the equilib-rium and growth at a long time step (150–300 s) (Jacobson,2005a). In WRF/Chem, different equilibrium modules areused in different aerosol modules. The inorganic aerosolequilibrium modules are the Model for an Aerosol React-ing System (MARS)-version A (MARS-A) of Binkowskiand Shankar (1995) in MADE/SORGAM, the Multicompo-nent Equilibrium Solver for Aerosols (MESA) with a newactivity coefficient module Multicomponent Taylor Expan-sion Method (MTEM) (MESA-MTEM) in MOSAIC, andISORROPIA in MADRID. Both MARS-A and ISORROPIAuse regime equilibrium, whereas MESA-MTEM does not.Sodium chloride is not treated in MARS-A but treated inISORROPIA and MESA-MTEM. Zhang et al. (2000) eval-uated five inorganic aerosol modules used in major 3-D airquality models including MARS-A and EQUISOLV II. Theyfound that MARS-A has the fastest computational speedbut it may not be applicable to dry areas with low rela-tive humidities (RHs) and coastal areas. Zhang and Jacob-son (2005a) evaluated ISORROPIA and EQUISOLV II inboth a box model with 11 200 test cases and a 3-D modelover continental US. While they found that ISORROPIAgives results that are consistent with those of benchmarkand EQUISOLV II under most conditions, larger bias mayoccur for RHs≤40 or ≥99 for most species, mainly be-cause of the use of an approximate treatment for water con-tent and solid-liquid equilibrium in the mutual deliques-cence region at moderate and low RHs (Ansari and Pandis,1999; Zaveri et al., 2008) and errors in activity coefficientsused at very high RHs. An improved ISORROPIA (version1.7) has been developed and implemented in WRF/Chem-



Table 5. Treatments of Aerosol Chemistry and Microphysics of Online Models.

ModelSystem

Inorganicaero. thermodynamicequilibrim

Secondaryorganicaerosolformation

New particleFormation

Condensationof gases onaerosols

Coagulation Gas/particlemass transfer


EQUISOLV II,major inorganic saltsand crustalspecies

Condensation;Dissolutionbased onHenry’s law(10–40 classes VOCs)

Binary homogeneousnucleation ofH2SO4 and H2Oof Vehkamaki et al.(2002), T- and RH-dependent;Ternary nucleationfrom Napari et al.(2002)

Dynamiccondensation ofall condensiblespecies based ongrowth law(e.g., H2SO4, VOCs)using theAnalyticalPredictorof Condensation(APC) with themovingcenter scheme

Sectional,multiple sizedistributions,Brownian diffusion,turbulent shear,turbulent inertialmotion,gravitational settling,diffusiophoresis,thermophoresis,electric charge,also accounts forvan der Waalsand viscous forces,and fractal geometry

Dynamic approachwith a long timestep (150–300 s)(PNG-EQUISOLV II)for alltreated species

WRF/Chem(Mesoscale)

MARS-A(SORGAM)MESA-MTEM(MOSAIC)ISORROPIA(MADRID)

Reversibleabsorption(8 classes VOCs)based onsmog-chamberdata (SORGAM)Absorption(MADRID1)and combinedabsorption anddissolution(MADRID2).No SOA treatmentin MOSAIC

Binaryhomogeneousnucleation ofH2SO4 andH2O ofKulmala et al.(1998 b) (SORGAM)and of McMurry,and Friedlander,(1979) (MADRID);T- andRH-dependent;sectional;different equationsin differentaero modules

Dynamiccondensation ofH2SO4 andVOCs using themodal approach ofBinkowski andShankar (1995)(SORGAM), ofH2SO4,MSA, and NH3using the AdaptiveStep Time-splitExplicitEuler Method(ASTEEM) method(MOSAIC), and ofvolatile inorganicspecies using theAPC with movingcenter scheme(MADRID)

Modal/Sectional(MADE/SORGAM,MOSAIC),single sizedistribution,fine modes only

1. Full equilibriumfor HNO3 andNH3 inMADE/SORGAMandall speciesin MADRID2. Dynamic forH2SO4 inMADE/SORGAM;Dynamic forall speciesin MOSAICand MADRID3. Hybrid inMADRID

CAM3(Global)

MOZART4 withregime equili. forsulfate, nitrate,and ammonium

Prescribed SOAyield for α-pinene,n-butane, and toluene

None Instantaneouscondensation ofinorganic species

None Full equilibriuminvolving (NH4)2SO4and NH4NO3

MIRAGE2(Global)

Sulfate assumedto be (NH4)2SO4,no nitrate

Prescribed SOAyield for monoter-penes

Binary homogeneousnucleation ofH2SO4 andH2O ofHarrington and Krei-denweis (1998);T- and RH-dependent

Dynamiccondensation ofH2SO4and MSA basedon Fuchs and Sutugingrowth law

Modal, single sizedistribution,fine modesonly; Browniandiffusion

Dynamic approachfor H2SO4and MSA

Caltechunified GCM(Global)

ISORROPIA withregime equili. forsulfate, nitrate,ammonium, sea-salt,and water

ReversibleAbsorption for5 biogenicVOC classes

None None None Full equilibriuminvolving (NH4)2SO4and NH4NO3

MADRID. MESA (Zaveri et al., 2005a) is designed to ef-ficiently solve the complex solid-liquid partitioning withineach aerosol size bin using a pseudo-transient continuationtechnique. MESA and EQUISOLV II are evaluated againstthe AIM Model III and they give overall similar results interms of both mass growth factors and performance statis-tics relative to the AIM Model III for the 16 cases tested inZaveri et al. (2005a). A major factor contributing to the dif-

ferences in simulated results from various aerosol thermody-namic modules is the activity coefficients used in these mod-ules. For example, EQUISOLV II and ISORROPIA accountfor temperature-dependence for all activity coefficients whensuch information are available, the activity coefficients usedin MESA, however, are limited for 298 K, which may in-troduce errors for their applications for upper troposphericand stratospheric conditions (e.g., the tropical tropopause



where the temperature may fall below 200 K and activitycoefficients of species may deviate largely from their val-ues at 298 K). While EQUISOLV II provides a generic codefor aerosol thermodynamic calculation, most other mod-ules (e.g., ISORROPIA and MESA) require non-trivial ef-forts to expand the system of equations for more speciesand/or other temperatures and/or the re-development of someparameterizations used.

Several major approaches have been used in 3-D modelsto simulate secondary organic aerosol (SOA) including sat-uration or fixed aerosol yield (e.g., Pandis et al., 1992), ab-sorption/adsorption (Pankow, 1994 a, b), dissolution (Jacob-son, 1997a), dynamic condensation (Jacobson, 1997a), andcombination of absorption and dissolution (Pun et al., 2002;Griffin et al., 2002). Both CAM3 and MIRAGE2 use pre-scribed aerosol yields for a few condensable volatile organiccompounds (VOCs), which is the simplest, computationallymost efficient approach but it does not provide a mechanisticunderstanding of SOA formation. GATOR-GCMOM simu-lates SOA formation from 10–40 classes VOCs via conden-sation and dissolution based on Henry’s law. Caltech uni-fied GCM simulates the formation of SOA based on a re-versible absorption of 5 classes of biogenic VOCs and ne-glect that from anthropogenic VOCs. In MADE/SORGAMin WRF/Chem, SOA formation via the reversible absorptionof 8 classes of VOCs is simulated based on smog-chamberdata of Odum et al. (1997) and Griffin et al. (1999). Thesame approach for SOA modeling has been used in an of-fline version of MOSAIC, which, however, has not been in-corporated into WRF/Chem for 3-D applications. Two ap-proaches are used to simulate SOA formation in WRF/Chem-MADRID (Zhang et al., 2004). MADRID 1 uses an absorp-tive approach for 14 parent VOCs (2 anthropogenic, and 12biogenic) and 38 SOA species (4 anthropogenic, and 34 bio-genic). MADRID 2 combines absorption and dissolution ap-proaches to simulate an external mixture of 42 hydrophilicand hydrophobic VOCs, which are grouped into 10 surrogatecompounds based on their affinity for water, origin, numberof carbon, volatility, and dissociation properties (Pun et al.,2002). MADRID 1 has been upgraded to Sesqui-MADRID(MADRID 1.5) and now treats phase separation (i.e., a rela-tively hydrophilic phase and a relatively hydrophobic phase)within the organic particulate phase when thermodynami-cally favorable (Pun et al., 2008). MADRID 2 has beenmodified to be compatible with any gas-phase mechanism(Pun et al., 2006). A variation of MADRID 2 that is compu-tationally efficient has been incorporated into the MesoscaleNonhydrostatic Chemistry (Meso-NH-C) model of Tulet etal. (2003) that couples meteorology and chemistry online(Tulet et al., 2005, 2006). Simulated SOA concentrations bymost 3-D models are, however, lower than observations forseveral reasons. For example, these models use the yields foraromatics and monoterpene oxidation under high NOx con-ditions (e.g., Odum et al., 1997; Griffin et al., 1999; Ng etal., 2007 a, b). Some SOA precursors in these models may

be missing (e.g., isoprene SOA is not simulated in MADE-SORGAM), which have been shown to be important at bothglobal and regional scales (e.g., Henze and Seinfeld, 2006;Zhang et al., 2007b).

New particle formation via binary homogeneous nucle-ation is simulated in all models except for CAM3, and thatvia ternary nucleation based on Napari et al. (2002) is onlysimulated in GATOR-GCMOM. Different models use differ-ent equations that account for the dependence of new parti-cle formation rates in different ways on number concentra-tion or critical vapor pressure of H2SO4, critical new par-ticle formation rate, temperature, and RH. The binary pa-rameterization of Harrington and Kreidenweis (1998) usedin MIRAGE2 is based on the calculations of nucleation ratesperformed by Jaecker-Voirol and Mirabel (1989), which cal-culates the absolute nucleation rates based on heteromolecu-lar homogeneous nucleation theory of the H2SO4–H2O sys-tem. The parameterizations of Kulmala et al. (1998) usedin MADE/SORGRAM, Wexler et al. (1994) used in MO-SAIC in WRF/Chem, and Vehkamaki et al. (2002) used inGATOR-GCMOM are derived based on the classical binaryhomogeneous nucleation model that simulates nucleation ki-netics and accounts for hydration. The parameterization ofKulmala et al. (1998) predicts binary nucleation rates upto 2–3 orders of magnitude lower than those predicted bymost other binary nucleation parameterizations due to thefact that its derivation contains mistakes in the kinetic treat-ment for hydrate formation (Vehkamaki et al., 2002; Noppelet al., 2002; Zhang and Jacobson, 2005b). The parameteri-zation of McMurry and Friedlander (1979) in WRF/Chem-MADRID uses an approach that simulates gas-to-particleconversion between nucleation of new particles and conden-sation on existing particles, which is a more realistic ap-proach than that based on the absolute prediction of a nu-cleation rate. While CAM3 assumes instantaneous conden-sation of inorganic species, other models simulate dynamiccondensation of condensable species based on similar growthlaws but with different numerical condensational algorithms.For example, GATOR-GCMOM and WRF/Chem-MADRIDuse the Analytical Predictor of Condensation (APC) withthe moving center scheme, WRF/Chem-MADE/SORGAMuses the modal approach of Binkowski and Shankar (1995),WRF/Chem-MOSAIC (version 2.2) uses the Adaptive StepTime-split Explicit Euler Method (ASTEEM) method, whichhas recently been updated to the Adaptive Step Time-splitEuler Method (ASTEM) method to reduce the stiffness moreeffectively using several methods and to allow the use oflonger time step (∼100 s) in an offline version of MOSAIC(Zaveri et al., 2008). Coagulation is currently not treatedin CAM3 but simulated with a modal approach with severallognormally-distributed modes in MIRAGE2, a sectional ap-proach with a number of size sections in GATOR-GCMOM,and both approaches in WRF/Chem (e.g., MADE/SORGAMuses the modal approach; MOSIAC and MADRID use thesectional approach). Different from other model treatments,



GATOR accounts for van der Waals and viscous forces,and fractal geometry in simulating coagulation among parti-cles from multiple size distributions (Jacobson and Seinfeld,2004). While van der Waals and fractal geometry may en-hance coagulation, viscous forces tend to retard the rate ofvan der Waals force enhancement in the continuum regime.

Three approaches are typically used to simulategas/particle mass transfer in 3-D air quality models:full equilibrium, dynamic, and hybrid. No condensationequation is explicitly solved in the full equilibrium approach(although a weighting factor based on condensationalgrowth law may be used to distribute the transferred massmaterial from gas to particulate phase over the particlesize distribution), whereas condensation is explicitly solvedfor all particles in the dynamic approach and for particleswith diameter greater than a threshold (a typical thresholdvalue of 1 to 2.5µm is assumed) in the hybrid approach.In such cases, condensation is a sub-process of gas/particlemass transfer. For gas/particle mass transfer, CAM3 andCaltech unified GCM use the simplest full equilibriumapproach. MIRAGE2 uses a dynamic approach for H2SO4and MSA. GATOR-GCMOM uses a computationally effi-cient dynamic approach with a long time step (150–300 s)(PNG-EQUISOLV II) for all treated species (Jacobson,2005a). In WRF/Chem, a full equilibrium is used for HNO3and NH3 in MADE/SORGAM. A dynamic approach isused for H2SO4 in MADE/SORGAM and all species inMOSAIC. In the dynamic approach of MOSAIC, ASTEEMis coupled with MESA to solve the dynamic gas-aerosolpartitioning over multiple size bins. Characteristic timesfor semi-volatile trace gases to reach equilibrium can varysignificantly (by up to several orders of magnitude) amongparticles with different sizes, making the coupled systemof ordinary differential equations for gas-aerosol masstransfer extremely stiff. ASTEEM is developed to reducethe stiffness of the system and improve computationalefficiency by allowing the solver to take longer time stepswith only a relatively small loss in accuracy. MADRIDoffers three approaches: full equilibrium, dynamic, andhybrid; their performance has been evaluated in Zhang etal. (1999) and Hu et al. (2008). The box MADRID testsof Hu et al. (2008) have shown that the bulk equilibriumapproach is computationally-efficient but fails to predictthe distribution of semi-volatile species (e.g., ammonium,chloride, and nitrate) because of the equilibrium and internalmixture assumptions. The hybrid approach exhibits the sameproblem for some cases as the bulk equilibrium approachsince it assumes bulk equilibrium for fine particles. Thekinetic approach predicts the most accurate solutions withvariable computational efficiencies depending on whether asmall time step is required.

3.5 Aerosol-cloud interactions and cloud processes

Table 6 summarizes the treatments of aerosol-cloud inter-actions and cloud processes used in the five models. Wa-ter uptake is a very important process affecting calculationsof both direct and indirect forcing. CAM3 simulates bulkequilibrium with RH for external mixtures only. MIRAGE 2and WRF/Chem-MOSAIC simulate hygroscopic growth inequilibrium with RH based on Kohler theory. Water up-take is calculated as a function of RH, the mean dry ra-dius, the relative contributions of each aerosol componentto the total particle hygroscopicity, and the aerosol watercontent from previous time step. Aerosol water contentin GATOR-GCMOM is calculated based on discrete size-resolved equilibrium using the Zdanovskii-Stokes-Robinson(ZSR) method (Zdanovskii, 1948; Stokes and Robinson,1966); it simulates the mutual deliquescent RH (MDRH).The ZSR method is also used to simulate aerosol water up-take in Caltech unified GCM. No hysteresis effect is ac-counted for in CAM3 and Caltech unified GCM, but it istreated in other models.

Activation of aerosol particles that can behave as CCN toproduce cloud droplets is an important process affecting sim-ulations of aerosol-cloud interactions, and aerosol direct andindirect forcing. CAM3 uses empirical, prescribed activatedmass fraction for bulk CCN. MIRAGE 2 and WRF/Chemuse a mechanistic, parameterized activation module that isbased on Kohler theory to simulate bulk CCN. In Kohler the-ory, the number of particles activated is expressed in terms ofsupersaturationS, which is primarily determined by aerosolproperties (i.e., number, size, and hygroscopicity) and up-draft velocity. Important parameters for activation such asthe peak supersaturation,Smax, mass of activated aerosols,and the size of the smallest aerosol activated are calculatedusing the parameterizations of Abdul-Razzak et al. (1998)and Abdul-Razzak and Ghan (2000) that relate the aerosolnumber activated directly to fundamental aerosol properties.The effects of Kelvin and Rault’s law for liquid activationare partially taken into account in those parameterizations.GATOR-GCMOM also simulates a mechanistic, size- andcomposition-resolved CCN/IDN based on Kohler theory. Athigh-resolution regional scales, the saturation ratios at equi-librium (S′) are determined from Kohler theory as a func-tion of aerosol particle composition and size, accounting forthe Kelvin effect and Raoult’s law for liquid activation andthe Kelvin effect for ice activation. Aerosol compositionof a given size affects the Kelvin term through the surfacetension and Raoult’s law through the molality term (Jacob-son et al., 2007). On the global scale and coarse regionalscales, the water vapor available for condensation is deter-mined from cumulus and stratus parameterizations. The cu-mulus parameterization treats subgrid clouds, and aerosolparticles are convected within each of these clouds. Liquidand ice from the cumulus/stratus parameterization are evap-orated/sublimated and regrown onto size- and composition-



Table 6. Treatments of Aerosol-Cloud Interactions and Cloud Processes of Online Models.

ModelSystem

Aerosolwateruptake

Aerosolactivationaero-CCN/IDN

In-cloudscavenging

Below-cloudscavenging

Coagulationinvolvinghydrometeor

Sedimentationof aerosolsand clouddroplets


Size-resolvedEquilibrium withRH; ZSRequation;simulatedMDRH;Hysteresisis treated

Mechanistic,size- andcomposition-resolvedCCN/IDNbased onKohler theory;accountingfor the Kelvineffect andRaoult’s lawfor liquidactivationand theKelvin effectfor iceactivation

Size-resolvedaerosolactivation;nucl.scavenging(rainout),autoconversionfor size-resolvedcloud droplets;precip. ratedependent onaerosol sizeand composition

Size-resolvedaerosol-hydrometeorcoag. (washout),calculated precip.rate dependenton aerosolsize andcomposition

Size-resolvedcoagulationbetweenhydrometeorsand betweenall aerosolsand allhydrometeors

Two-momentdiscretesize-dependentsedimentationfor allaerosolparticles andhydrometeors(mass and number)that varywith altitude;sedimentationbelow cloudleads toshrinkageas a functionof drop size

WRF/Chem(Mesoscale)

The sameas MIRAGE2but sectional(MOSAIC)

The sameas MIRAGE2but sectional(MOSAIC);bulk CCN only

The sameas MIRAGE2but sectional

Similar toMIRAGE2but sectional

Similar toMIRAGE2but sectional

Two-momentsedimentationfor aerosolparticles(mass and number)at surface;sedimentationfor allhydrometeorsor a subsetof them,depending onmicrophysicsschemes

CAM3(Global)

For externalmixtures only,bulkequilibrium withRH, nohysteresis

Empirical,prescribedactivated massfraction;bulk CCN only

Prescribedbulk activation,autoconversion,precip. rateindependent onaerosols

Prescribedbulk scav.efficiency,no-sizedependence

None Bulkcloud/icesedimentation;sedimentationbelow cloudleads tocompleteevaporation/sublimation

MIRAGE2(Global)

Bulkequilibriumwith RHbased onKohler theory,Hysteresisis treated

Mechanistic,parameterizedmodal activationbased onKohler theory;bulk CCN only;partiallyaccountingfor theKelvin effectand Raoult’slaw forliquidactivation

Modalactivation(nucleation)scavenging,Browniandiffusion(for activated parti-cles),autoconversionand collectionfor bulkcloud droplets,precip. rateindependentof aerosols

Calculatedmodalscavenging coeff.using aparameterizationof thecollectionefficiencyof aerosolparticlesby raindrops,with sizedependence

Modalcoagulationbetweencloud droplets,betweencloud dropletsand precipitatingparticles,and betweenaerosol andprecipitatingparticles

Two-momentsedimentationfor aerosolparticles(mass and number)at thesurface; nosedimentationfor clouddroplets/ices,cloud-borneand ice-borneaerosolparticles.

CaltechunifiedGCM(Global)

Bulkequilibrium,ZSR equation,no hysteresis

None Bulkautoconversion;nucl. scavengingwith prescribedscavengingcoefficientfor sea-saltand dust anda first-orderprecipitation-dependentparameterizationfor otheraerosols; precip.rate independentof aerosols

First-orderprecipitation-dependent bulkparameterization;calculatedscavengingefficiencywith sizedependence

None Implicitlyaccountedfor in aparameterizationof thelimitingautoconversionrate



resolved aerosol particles (Jacobson, 2003c). One differ-ence between the treatments in GATOR-GCMOM and MI-RAGE2 is that the MIRAGE activation parameterization ne-glects size-dependence of the water vapor diffusivity coef-ficient and mass transfer coefficient, which may lead to anunderestimation of cloud droplet number concentration. Inaddition, it does not treat the kinetic effect (i.e., mass trans-fer limitation) for larger particles for which the equilibriumKohler theory may be inappropriate. Such size-dependenceand kinetic effects are accounted for in GATOR-GCMOM.Aerosol-cloud interaction is currently not treated in Caltechunified GCM.

Aerosols are removed through dry deposition in the ab-sence of hydrometerors and through wet deposition follow-ing in- and below- cloud scavenging, in addition to be ac-tivated as CCN to form cloud droplets. After activation,cloud droplets (and cloudborne aerosol particles) are re-moved via autoconversion (i.e., the collision/coalescence ofcloud drops to become rain drops and get into precipitation)and via collection/accretion by (existing) precipitation (rain,snow, graupel). CAM3 assumes that in-cloud scavenging oc-curs via prescribed activation and autoconversion. Caltechunified GCM treats autoconversion and nucleation scaveng-ing with prescribed scavenging coefficient for sea-salt anddust and a first-order precipitation-dependent parameteriza-tion for other aerosols. The in-cloud scavenging processes inMIRAGE2 and WRF/Chem include activation (nucleation)scavenging, Brownian diffusion (for activated particles), au-toconversion, and collection. The dependence of autocon-version on droplet number is neglected in both models. Allthose processes are included for discrete size-resolved cloudsin GATOR-GCMOM. Note that autoconversion is treatedsomewhat differently in GATOR-GCMOM as in other mod-els because of differences in the cloud treatments. Clouddroplets are treated to be size-resolved in GATOR-GCMOMbut bulk in other models. Consequently, other models treatautoconversion for bulk cloud droplets whereas GATOR-GCMOM treats coagulation for discrete size-resolved clouddroplets into rain drops/ice crystals (which is analogous toautoconversion for bulk clouds). The dependence of precipi-tation rates on discretely size resolved aerosols via nucleationscavenging and impact scavenging is taken into account inGATOR-GCMOM, but are neglected in other models. Themain differences between treatments of cloud-processing ofaerosols in GATOR-GCMOM and other models are (1) othermodels treat removal of aerosols as an empirical function ofthe rainfall rate without physical interactions of size-resolvedaerosols with size-resolved rainfall, and (2) other models donot always track all the aerosol components that the cloudsformed on within size resolved cloud drops.

For below-cloud scavenging, CAM3 prescribes scaveng-ing efficiencies. MIRAGE2 and WRF/Chem calculate ascavenging coefficient (=scavenging rate/precipitation rate)using a parameterization of the collection efficiency ofaerosol particles by rain drops (due to convective Brownian

diffusion and gravitational/inertial capture). Caltech unifiedGCM assumes the first-order precipitation-dependent scav-enging parameterization, whereas GATOR-GCMOM sim-ulates discrete size-resolved aerosol-hydrometeor coagula-tion (washout). The dependence of below-cloud scaveng-ing and precipitation rates on aerosol size and compositionis accounted for in GATOR-GCMOM but either partially(e.g., Caltech unified GCM calculates size-dependent scav-enging efficiency) or completely neglected in other models.Among the five models, GATOR-GCMOM is the only modelthat treats coagulation between different size sections fromdifferent size distributions for various hydrometeors (e.g.,liquid-liquid, liquid-ice, liquid-graupel, ice-ice, ice-graupel,graupel-graupel) and that between aerosols and hydromete-ors. MIRAGE2 and WRF/Chem simulate coagulation be-tween cloud droplets, between cloud droplets and precipitat-ing particles, and between aerosol and precipitating particlesfor one size distribution of each type of hydrometeors.

Sedimentation refers to the layer-by-layer sinkingof aerosol particles, hydrometeor particles (e.g., clouddrops/ice, cloud-borne and ice-borne particles, and precipi-tating particles) as a function of their sizes. Sedimentation ofcloud droplets and precipitating particles to the ground in thebottom layer is precipitation if a model treats sedimentationlayer by layer. GATOR-GCMOM treats layer by layersedimentation of discrete size-resolved aerosol particles,liquid, ice, graupel particles, and their chemical inclusionswith fall speeds for both mass and number concentrations(i.e., so-called two-moment methods) as a function oftheir sizes. As droplets fall below clouds, they shrinkas a function of size. Some may completely evaporate,releasing their aerosol cores back to the air. Some may hitthe ground as precipitation. CAM3 treats sedimentationof bulk mass and number concentrations of liquid and iceparticles, each with a single fall speed that is calculatedas a function of a mass-weighted effective radius of iceparticles (Boville et al., 2006). For bulk ice, the effectiveradius is calculated for a size distribution that is assumed tobe a function of temperature only. For bulk liquid, no sizedistribution is assumed; the effective radius is determinedfrom the bulk liquid water mass and the total numberconcentration of particles. Liquid and ice particles fallingfrom one layer to the next within a cloud do not coagulate asa function of size. All hydrometeors falling below a cloudare evaporated/sublimated completely without releasingaerosol cores. No precipitation resulted from sedimentationunless the cloud exists in the bottom layer (Note thatprecipitation is calculated as a separate autoconversion inCAM3). MIRAGE2 also treats two-moment sedimentationfor aerosol particles, but it does not treat sedimentation ofcloud droplet/ice and cloud-born and ice-born particles.Depending on schemes for cloud microphysics used inWRF/Chem, the sedimentation process is treated for allthe hydrometeor categories treated or a subset of themfor both mass and number concentrations (Skamarock et



al., 2005). Droplet sedimentation is not explicitly treatedin Caltech unified GCM because it does not resolve thescales of vertical motion relevant to sedimentation; it is,however, implicitly accounted for by parameterizing thelimiting autoconversion rate as a decreasing function ofthe large scale vertical velocity (Del Genio et al., 1996).A discrete cloud size distribution as it is used in GATOR-GCMOM is necessary to realistically simulate all cloudmicrophysical processes (e.g., condensation/evaporation,deposition/sublimation, collision-coalescence, contact freez-ing, rainout, washout, sedimentation) from first principlesrather than parameterizations. The droplet sedimentationtreatment in CAM3 is not physical and prevents an accuratesimulation of the physical feedbacks of aerosol particles toclimate.

4 Case studies

To illustrate the importance of the feedbacks discussed pre-viously, several case studies on some of the feedbacks us-ing some of the aforementioned models are provided below.These include the feedbacks of aerosols to PBL meteorologyby WRF/Chem-MADRID, the feedbacks of aerosols to windfields and precipitation by GATOR-GCMOM, and the feed-backs of aerosol/cloud to indirect aerosol radiative forcing byMIRAGE2 and CAM3. These studies represent the currentstatus of model capability in simulating such feedbacks withthe state-of-the-science treatments.

4.1 WRF/Chem-MADRID

WRF/Chem-MADRID has been applied to simulate a 5-day episode (12:00 UTC 28 August through 12:00 UTC2 September of 2000) from the Texas Air Quality Study(TexAQS-2000) in the southern US. The TexAQS-2000 wascarried out around the Houston area where exceedance of theNational Ambient Air Quality Standard (NAAQS) of 120 ppbO3 occurs most frequently and VOC reactivities are typicallymuch higher than in other urban areas in the US WRF/Chemuses mass (hydrostatic pressure) coordinates. The horizontalgrid spacing is 12-km and the vertical resolution is 57 lay-ers from surface to tropopause with vertical intervals vary-ing from 15 m in the surface layer to 600–680 m near/at thedomain top (∼16km). The initial conditions, boundary con-ditions, and emissions are the same ones as the WRF/Chemsimulations with MOSAIC described in Fast et al. (2006).Cloud barely occurred during this episode. The cloud mi-crophysical scheme is thus turned off. No aerosol-cloud in-teraction and aerosol indirect effects were simulated. Thesimulation results have been evaluated against in situ obser-vations for gas-phase species (e.g., O3, SO2, nitrogen diox-ide (NO2), and nitric oxide (NO)), PM2.5, and its composi-tion and remote sensing measurements (e.g., aerosol opticaldepths) (Zhang et al., 2005c, 2007; Hu et al., 2006).

L a p o r t e ( H O 8 H ) o n A u g u s t 2 9

0

1000

2000

3000

4000

0 4 8 12 16 20 24 28 32

C o n c e n t r a t i o n o f P M2 . 5

, μg m - 3

Height, m

06 LST

08 LST

11 LST

14 LST

17 LST

Laporte (HO8H) on August 29

0

1000

2000

3000

4000

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Differences in water vapor mixing ratio,

g/kg

Height, m

06 LST

08 LST

11 LST

14 LST

17 LST

Fig. 2. The spatial distribution of the 24-h average PM2.5 concen-trations and the 24-h average wind fields predicted by WRF/Chem-MADRID on 29 August 2000 (Zhang et al., 2005a).

Figure 2 shows the spatial distribution of the predicted 24-h average PM2.5 concentrations and the 24-h average windfield on 29 August (central daylight time (CDT)), 2000. Thepredicted PM2.5 distribution is consistent with the patterns ofemissions and wind field. The emissions of primary PM2.5species such as BC and other unknown inorganic PM2.5 arehigh in Houston, the emissions of SO2 are high in BatonRouge and the emissions of CO and NOx are relatively highin Dallas, resulting in relatively high PM2.5 concentrationsin those cities and their vicinity. The normalized mean bi-ases (NMBs) of the hourly O3 and PM2.5 predictions are19.8% and 41.7%, indicating a moderate overprediction thatcan be attributed to several factors including overestimationof primary BC and organic matter (OM) emissions and highaerosol boundary conditions. Figure 3 shows the vertical pro-files of PM2.5 concentrations and the differences in verticaltemperature (T) and water vapor (Qv) mixing ratio betweensimulations with and without PM at five different times onAugust 29 at LaPorte that is located in the east of Houstonat the coastal area of the Galveston Bay. As shown, PM2.5concentrations at surface and in the PBL vary significantlyfrom time to time during a day, depending on magnitudesand timing of precursor emissions and related meteorological




0

1000

2000

3000

4000

0 4 8 12 16 20 24 28 32

Concentration of PM2.5

, μg m-3

Height, m

06 LST

08 LST

11 LST

14 LST

17 LST

Laporte (H08H) on August 29

0

1000

2000

3000

4000

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

Differences in temperature,degree C

Height, m

06 LST

08 LST

11 LST

14 LST

17 LST


0

1000

2000

3000

4000

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Differences in water vapor mixing ratio, g/kg

Height, m

06 LST

08 LST

11 LST

14 LST

17 LST

Fig. 3. The vertical distributions of the hourly PM2.5 concentrations and differences in vertical distributions of temperatures and water vapormixing ratios between simulation with and without aerosols by WRF/Chem-MADRID at La Porte, TX at five times ( 6 a.m., 8 a.m., 11 a.m.,2 p.m., and 5 p.m.) on 29 August 2000 (Zhang et al., 2005c).

conditions such as atmospheric stability, the depth of mix-ing height, and temperature. The surface PM2.5 reaches thehighest at 6 a.m. due to high emissions of primary PM2.5and precursors of secondary PM2.5 from motor vehicles andrelatively-shallow mixing height. The PM2.5 concentrationin the PBL reaches the highest at 2 p.m. due likely to the ef-fect of bay breeze. As expected, T and Qv respond stronglyto changes in PM2.5 concentrations, with maximum changescoincide with maximum gradients in PM2.5 concentrationsin the PBL. T reduces by up to 0.18◦C at/near surface butincreases by 0.16◦C in PBL. Water vapor mixing ratio in-creases by 3.2% at/near surface but decreases by 3% in thePBL. The relatively high PM2.5 concentrations at/near sur-face reduce net downward solar/thermal-IR radiation, whichin turn causes a decrease in T and an increase in Qv at/nearsurface. Opposite changes in the PBL may be caused byradiation absorption of particles and advection of long- ormoderately-lived greenhouse gases that absorb thermal-IRradiation emitted by particles aloft.

4.2 GATOR-GCMOM

GATOR-GCMOM has been applied to simulate the effectof aerosol feedbacks into regional climate changes over aglobal domain at a horizontal resolution of 4◦ SN×5◦ WEand two nested domains: a so-called California (CA) Gridat a resolution of 0.2◦×0.15◦(∼21.5 km×14.0 km) and a so-called the South Coast Air Basin Grid: at a resolution of0.045◦×0.05◦(∼4.7 km×5 km) (Jacobson et al., 2007). Thevertical resolutions are 39 sigma levels up to 0.425 hPa forthe global domain and 26 layers up to 103.5 hPa, each match-ing the bottom 26 global layers (with five layers in the bottom1 km for all domains). The baseline simulations were con-

ducted for two 1-month periods in 1999: February and Au-gust. In sensitivity simulations, emissions of anthropogenicaerosol particles and their precursor gases (AAPPG) such asBC, OC, sulfate, nitrate, fugitive dust, SOx, NOx, NH3, andreactive organic gases (ROGs) were turned off. Over the LAbasin, AAPPG is found to reduce net downward surface to-tal solar irradiance, near-surface temperatures, and surfacewind speeds; increase RHs, aerosol and cloud optical depths,cloud fractions, cloud liquid water; and either increase or de-crease precipitation depending on location and magnitude ofprecipitation intensity.

Figure 4 shows the effect of AAPPG on near-surface windspeeds and vertical profiles of wind speeds over Californiagrid simulated by GATOR-GCMOM in February and Au-gust 1999. Aerosols decrease surface wind speed but in-crease boundary-layer wind speed. The decease is drivenprimarily by two factors: the cooling at the surface due tothe reduction in surface solar radiation and the warming inthe upper boundary-layer due to the heating caused by theabsorbing aerosols. Both factors stabilize the air, reducingturbulence which in turn reduces vertical flux of horizontalmomentum, thus slowing transfer of fast winds aloft to thesurface (Jacobson et al. 2007). Figure 5 shows the effect ofAAPPG on precipitation for the South coast, CA and the CAgrids. AAPPG decreases precipitation in the LA basin andthe mountains beyond the basin in February. In August, whenprecipitation is low, most reductions occur offshore and inthe foothills of the San Bernardino Mountains. Some precip-itation increases are found on the downslope sides of the SanBernardino and San Gabriel Mountains. Those results areconsistent with the findings of Givati and Rosenfeld (2004,2005).



Feb. 60-170 m wind speed diff. (m/s) (-0.025) Aug. 60-170 m wind speed diff. (m/s) (-0.044)

Figure 4. Differences in the spatial distributions of near-surface wind speeds over California grid and Fig. 4. Differences in the spatial distributions of near-surface wind speeds over California grid and in the domainwide-average verticaldistributions of wind speeds between simulation with and without AAPPG by GATOR-GCMOM in February and August 1999. The contoursin black lines in the spatial distribution plots indicate topography in meters (provided by M. Z. Jacobson, Stanford University, 2007).

4.3 CAM3 and MIRAGE2

3-year global simulations after 4-month spin-up were con-ducted with CAM3 and MIRAGE2 to understand the dif-ferences in simulated aerosol direct and indirect forcing dueto different aerosol and cloud microphysical treatments. Nonudging was used in those simulations. The horizontal reso-lution is 4◦ latitude×5◦ longitude and the vertical resolutionis 26 layers from surface to 3.5 hPa. Baseline simulations(CAM3 B and MIRAGE2B) were conducted with defaultaerosol modules (MOZART4 in CAM3 and PNNL’s aerosolmodule in MIRAGE2, see major differences in Tables 2–6).Four sensitivity simulations were conducted: a CAM3 sim-ulation with constant droplet sedimentation (CAM3S1), aCAM3 simulation with the same configurations as CAM3S1but offline coupling (CAM3S2), a MIRAGE2 simulationwith the same configurations as MIRAGE2B but with offlinecoupling (MIRAGE2S1), and a CAM3 simulation with thesame configurations as CAM3S2 but with PNNL’s aerosolmodule in replacing MOZART4 (CAM3S3).

Figure 6 shows results from those simulations. The firstaerosol indirect effect (FAIE) from CAM3B is much largerthan that from MIRAGE2B (3.2 vs. 0.38 W m−2), the pre-diction of MIRAGE2 B is much closer to the total aerosol

indirect forcing of 0.75 W m−2 estimated by IPCC (2007).MIRAGE2 has no droplet sedimentation. Compared withresults using bulk sedimentation that is calculated based onmass-weight effective radius of liquid and ice particles, themagnitude of FAIE in CAM3 decreases by∼30% with aconstant sedimentation velocity because sedimentation is re-duced. While this result demonstrates the sensitivity of simu-lated FAIE to droplet sedimentation treatments, neither treat-ments (i.e., bulk or constant) are realistic because of theuse of empirical parameterizations instead of the first prin-ciples that treat the sedimentation velocity of particles of in-dividual size. Both online and offline simulations use thesame monthly mean aerosol concentrations. But on shortertime scales the online simulation has variability so that lessaerosol is present under cloudy conditions, due to enhancedscavenging in clouds. As expected, using an offline aerosolcalculation increases magnitude of FAIE in both CAM3and MIRAGE2 because of increased aerosol presence undercloudy conditions. The use of MIRAGE2 aerosol modulein offline CAM3 significantly reduces FAIE in CAM3, sug-gesting that the addition of an aerosol treatment that allowsaerosol size distribution to shift with increasing emissions islikely to produce a smaller indirect effect, particularly whenit is interactive (Ghan, 2007).



Feb. precipitation diff. (mm/day) (-0.026) Aug. precipitation diff. (mm/day) (-0.033)

Feb. precipitation diff. (mm/day) (-0.029) Aug. precipitation diff. (mm/day) (-0.005)

Figure 5. Differences in the spatial distributions of precipitation over (a) California grid, and (b) the Fig. 5. Differences in the spatial distributions of precipitation over(a) California grid, and(b) the South Coast grids between simulationwith and without AAPPG by GATOR-GCMOM in February and August 1999. The contours in black lines indicate topography in meters(provided by M. Z. Jacobson, Stanford University, 2007).

5 Major challenges and future directions

Significant progress has been made in the past twodecades in the development of online-coupled climate-(or meteorology-) chemistry models and their applicationfor simulating global/regional climate, meteorology, and airquality, as well as the entire earth system. However, severalmajor challenges exist for further model development, im-provement, and application.

First, accurately representing climate-aerosol-chemistry-cloud-radiation feedbacks in 3-D air quality/climate mod-els will remain a major scientific challenge in develop-ing a future generation of online-coupled models for theyears to come, as many online-coupled models are cur-rently not significantly- or fully-coupled, in particular, suchfeedbacks are not fully represented in many online-coupledmodels. There are several key issues associated with suchneeds. For example, performing an online calculation ofall meteorologically-dependent emissions is necessary in allonline-coupled models. There is a critical need for furtherimprovement of model treatments of key processes such asthe size-/composition-resolved aerosol/cloud microphysicsfor multiple size distributions (e.g., new particle formation,SOA, and aerosol/cloud interactions) and aerosol-cloud in-teractions, as well as subgrid variability associated with these

processes. In addition, the scientific understanding of thetwo-way/chain effects among climate, meteorology, chem-istry, aerosol, cloud and radiation will continue to be neededfor their accurate representations in online-coupled mod-els. Incomplete and/or inaccurate treatments of model inputs(e.g., emissions) and physics treatments (e.g., aerosol/cloudmicrophysics and feedbacks) will contribute to the model un-certainties to a large extent.

Second, representing scientific complexity within thecomputational constraint will continue to be a technical chal-lenge. Key issues include (1) the development of bench-mark model and simulation and the use of available measure-ments to characterize model biases, uncertainties, and sensi-tivity and to develop bias-correction techniques (e.g., chem-ical data assimilation); (2) the optimization/parameterizationof model algorithms with an acceptable accuracy.

Third, integrated model evaluation and improvement, lab-oratory/field studies for an improved understanding of majorproperties/processes will also post significant challenges, asthey involve researchers from multiple disciplinaries and re-quire a multidisciplinary and or interdisciplinary approach.Key issues include (1) continuous operation of monitoringnetworks and remote sensing instrument to provide real-time data (e.g., the AirNow surface monitoring networkand Satellite) for data assimilation/model evaluation, (2)



First Indirect Radiative Forcing (W m-2) of anthropogenic sulfate

-4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0

CAM

CAM constant droplet

sedimentationCAM offline CAM

aerosolCAM offline MIRAGE

aerosolMIRAGE offline MIRAGE

aerosolMIRAGE

Fig. 6. Global first indirect effect of anthropogenic sulfate sim-ulated by baseline and sensitivity simulations of CAM3 and MI-RAGE2 (Ghan, 2007, inclusion with permission of S. J. Ghan, Pa-cific Northwest National Laboratory, 2007).

the development of process-oriented models to isolate com-plex feedbacks among various modules/processes in online-coupled models, (3) carefully-designed module/model inter-comparison to understand mechanistic differences in variousmodules embedded in online-coupled models and the resul-tant differences in simulated feedbacks by the 3-D models.Such comparisons should be conducted using both 0-D (i.e.,conducting box-model comparisons for different gas-phasechemical mechanisms and aerosol modules that are used inWRF/Chem), 1-D, and 3-D models (e.g., comparing modelperformance of several online models against observationaldata for the same episode) when possible.

Fourth, a unified modeling system that allows a sin-gle platform to operate over the full scale will represent asubstantial advancement in both the science and the com-putational efficiency. Major challenges include globaliza-tion/downscaling with consistent model physics and two-way nesting with mass conservation and consistency. Theonly such model that exists is GATOR-GCMOM, althoughother global-through-urban fully-coupled models such as theglobal-to-urban WRF/Chem (GU-WRF/Chem) are being de-veloped (e.g., O’Connor et al., 2006; Zhang et al., 2008b).Such an unified global-through-urban scale modeling systemallows a single platform to operate over the full scale. It rep-resents a substantial advancement in both the science and thecomputational efficiency, with a new scientific capability forstudying important problems that require a consideration ofmulti-scale feedbacks. For example, locally-emitted air pol-lutants can affect human health at a neighborhood-scale andair quality and climate at all scales and the changes in climatein turn affect further emissions of biogenic species; locallylifted dust particles can affect local and global circulations,which in turn affects their further lifting.

Finally, integrated earth system modeling for multi-media(e.g., atmosphere, biosphere, ocean, land surface, etc.) willrepresent models of next generation that can best replicatehuman’s environment. Most current earth system modelsfor atmosphere-land surface-ocean do not include detailedchemistry, aerosol, and cloud treatments and biogeochemicalcycles. The integration of such complexities into the earthsystem models will pose unprecedented challenges for theentire scientific communities.



Appendix A

List of Acronyms and Symbols

Acronym Definition

3-D three-dimensionalAAPPG the anthropogenic aerosol particles and their precursor gasesAIM2 the Aerosol Inorganics Model version 2APC the Analytical Predictor of CondensationASTEEM the Adaptive Step Time-split Explicit Euler MethodASTEM the Adaptive Step Time-split Euler MethodARW the Advanced Research WRF with the Eulerian MassBC black carbonCACM the California Atmospheric Chemical MechanismCAM3 (Global) the Community Atmospheric Model v. 3CB05 the 2005 version of Carbon Bond mechanismCBM-EX The Stanford University’s extended Carbon Bond mechanismCBM-Z the Carbon-Bond Mechanism version ZCCM the NCAR Community Climate ModelCCN cloud condensation nucleiCDT central daylight timeCFCs chlorofluorocarbonsCH4 methaneCMAQ the EPA’s Community Multiple Air QualityCMU Carnegie Mellon UniversityCO carbon monoxideCO2 carbon dioxideCTMs chemical transport modelsDEMA the iterative dynamic effective medium approximationDMS dimethyl sulfideEQUISOLV II the EQUIlibrium SOLVer version 2EPA the US Environmental Protection AgencyGCM general circulation modelGAQMs global air quality modelsGATORG the Gas, Aerosol, TranspOrt, Radiation, and General circulation modelGATOR-GCMOM(Global-through-urban)

the Gas, Aerosol, TranspOrt, Radiation, General Circulation, Mesoscale, Ocean Model

GATOR/MMTD(or GATORM)

the gas, aerosol, transport, and radiation air quality model/a mesoscale meteorological and tracer dispersion model

GChM the PNNL Global Chemistry ModelH2O waterH2O2 hydrogen peroxideHO2 hydroperoxy radicalH2SO3 sulfurous acidH2SO4 sulfuric acidIDN Ice Deposition NucleiIPCC Intergovernmental Panel on Climate ChangeISORROPIA “equilibrium” in Greek, refers to The ISORROPIA thermodynamic moduleLA Los AngelesMADE/SORGAM the Modal Aerosol Dynamics Model for Europe (MADE) with the secondary organic aerosol model (SORGAM)MADRID the Model of Aerosol Dynamics, Reaction, Ionization, and DissolutionMARS-A the Model for an Aerosol Reacting System (MARS) – version AMCCM(or MM5/Chem)

The Multiscale Climate Chemistry Model

MESA the Multicomponent Equilibrium Solver for AerosolsMM5 the Penn State University (PSU)/NCAR mesoscale modelMIRAGE the Model for Integrated Research on Atmospheric Global ExchangesMOSAIC the Model for Simulating Aerosol Interactions and Chemistry



Acronym Definition

MOZART4 the Model for Ozone and Related Chemical Tracers version 4MSA methane sulfonic acidMTEM The Multicomponent Taylor Expansion MethodNAAQS the National Ambient Air Quality StandardNCAR the National Center for Atmospheric ResearchNARE the North Atlantic Regional ExperimentNH4NO3 ammonium nitrate(NH4)2SO4 ammonium sulfateNMBs normalized mean biasesNMM the Nonhydrostatic Mesoscale ModelNO3 nitrate radicalNO nitric oxideNO2 nitrogen dioxideNOx nitrogen oxidesN2O nitrous oxideNOAA the National Oceanic and Atmospheric AdministrationO3 ozoneOC organic carbonODEs ordinary differential equationsOH hydroxyl radicalOM organic matterPAN peroxyacetyl nitratePBL the planetary boundary layerPM2.5 particles with aerodynamic diameters less than or equal to 2.5µmPNNL the Pacific Northwest National laboratoryQv water vaporRACM the Regional Atmospheric Chemistry MechanismRADM2 the gas-phase chemical mechanism of Regional Acid Deposition Model, version 2RHs relative humiditiesRIs refractive indicesROGs reactive organic gasesRRTM the Rapid Radiative Transfer ModelS(IV) dissolved sulfur compounds with oxidation state IVSO2 sulfur dioxideSOA secondary organic aerosolSTAR the US EPA-Science to Achieve Results programT temperatureTUV the Tropospheric Ultraviolet and Visible radiation modelUCLA-GCM the University of Los Angeles General Circulation ModelVOC volatile organic compoundWRF/Chem the Weather Research Forecast model with ChemistryZSR Zdanovskii-Stokes-Robinson

Acknowledgements. The work was supported by the US EPA-Science to Achieve Results (STAR) program (Grant # G6J10504),the US EPA/Office of Air Quality Planning & Standards via RTIInternational contract #4-321-0210288, the NSF Career AwardNo. Atm-0348819, and the Memorandum of Understanding be-tween the US Environmental Protection Agency (EPA) and the USDepartment of Commerce’s National Oceanic and Atmospheric Ad-ministration (NOAA) and under agreement number DW13921548.Thanks are due to S. Ghan and R. Easter at PNNL, M. Jacobsonat Stanford University, and A. Baklanov at Danish MeteorologicalInstitute for helpful discussions for MIRAGE/CAM3, GATOR-GCMOM, and early online models in Russia, respectively. Thanks

are also due to (in alphabetical order) A. Baklanov, R. Easter,J. Fast, and W. I. Gustafson Jr, C. J. Jang, B. Langmann, M. Leriche,C. Seigneur, R. Zaveri, and two anonymous referees for their com-ments online and offline regarding the author’s paper published inAPCD.

Edited by: H. Wernli



References

Abdul-Razzak, H., Ghan, S. J., and Rivera-Carpio, C.: A parameter-ization of aerosol activation: 1. Single aerosol type, J. Geophys.Res., 103, 6123–6132, 1998.

Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosolactivation: 2. Multiple aerosol types, J. Geophys. Res., 105,6837–6844, 2000.

Abdul-Razzak, H. and Ghan, S. J.: A parameterization ofaerosol activation: 3. Sectional representation, J. Geophys. Res.,107(D3), doi:10.1029/2001JD000483, 2002.

Ackermann, I. J., Hass, H., Memmesheime, M. R., Ebel, A.,Binkowski, F. S., and Shankar,U.: Modal aerosol dynamicsmodel for Europe: Development and first applications, Atmos.Environ., 32(17), 2981–2999, 1998.

Aloyan, A. E., Baklanov, A. A., and Penenko, V. V.: Fictitious re-gions in numerical simulation of quarry ventilation, Soviet Me-teorology and Hydrology, 7, 32–37, 1982.

Aloyan, A. E., Baklanov, A. A., and Penenko, V. V.: Fictitious re-gions in numerical simulation of quarry ventilation, Soviet Me-teorology and Hydrology, 7, 32–37, 1982.

Arakawa, A. and Lamb, V. R.: Computational design of the basicdynamical processes of the UCLA general circulation model, in:Methods in Computational Physics, edited by: Chang, J., Aca-demic Press, New York, 173–265, 1977.

Arakawa, A. and Lamb, V. R.: A potential enstrophy and en-ergy conserving scheme for the shallow water equations, Mon.Weather Rev., 109, 18–36, 1981.

Arteta, J., Cautenet, S., Taghavi, M., and Audiffren, N.: Impact oftwo chemistry mechanisms fully- coupled with mesoscale modelon the atmospheric pollutants distribution, Atmos. Environ., 40,7983–8001, 2006.

Atwater, M. A.: Thermal changes induced by urbanization and pol-lutants, J. Appl. Meteorol., 14, 1061–1071, 1975.

Audiffren, N., Buisson, E., Cautenet, S., and Chaumerliac, N.: Pho-tolytic impact of a stratocumulus cloud layer upon the chemistryof an offshore advected plume of pollutants during the NARE1993 intensive experiment: a numerical study, Atmos. Res., 70,89–108, 2004.

Austin, J., Butchart, N., and Shine, K. P.: Possibility of an Arc-tic ozone hole in a doubled-CO2 climate, Nature, 360, 221–225,1992.

Austin, J. and Butchart, N.: A three-dimensional modelingstudy of the influence of planetary wave dynamics on polarozone photochemistry, J. Geophys. Res., 97(D9), 10 165–10 186,doi:10.1029/92JD00505, 1992.

Austin, J., Knight, J., and Butchart, N.: Three-dimensional chemi-cal model simulations of the ozone layer: 1979–2015, Q. J. Roy.Meteor. Soc., 126, 1533–1556, 2000.

Austin, J., Shindell, D., Beagley, S. R., Bruhl, C., Dameris, M.,Manzini, E., Nagashima, T., Newman, P., Pawson, S., Pitari, G.,Rozanov, E., Schnadt, C., and Shepherd, T. G.: Uncertainties andassessments of chemistry-climate models of the stratosphere, At-mos. Chem. Phys., 3, 1–27, 2003,http://www.atmos-chem-phys.net/3/1/2003/.

Baklanov, A.: Numerical Modelling in Mine Aerology, Apatity:USSR Academy of Science, 200 pp. (in Russian), 1988.

Baklanov, A.: Mathematical modelling of the influence of miningon the environment Leeds University Mining Association, editedby: Dowd, P. A., 123–131, 1990.

Baklanov, A., Gross, A., and Sørensen, J. H.: Modelling and fore-casting of regional and urban air quality and microclimate, J.Computational Technologies, 9, 82–97, 2004.

Baklanov, A.: Urban air flow researches for air pollution, emer-gency preparedness and urban weather prediction, chapter 9 in:Flow and transport processes with complex obstructions: Ap-plications to cities, vegetative canopies and Industry, edited by:Gayev, Y. A. and Hunt, J. C. R., NATO Science Series II, Math.,Phys. and Chem., 236, Springer, 311–357, 2007.

Baklanov, A., Fay, B., and Kaminski, J.: Overview of existing in-tegrated (off-line and on-line) mesoscale systems in Europe, Re-port of Working Group 2, April, http://www.cost728.org, 2007a.

Baklanov, A., Hanninen, O., Slørdal, L. H., Kukkonen, J., Bjergene,N., Fay, B., Finardi, S., Hoe, S. C., Jantunen, M., Karppinen, A.,Rasmussen, A., Skouloudis, A., Sokhi, R. S., Sørensen, J. H.,and Ødegaard, V.: Integrated systems for forecasting urban me-teorology, air pollution and population exposure, Atmos. Chem.Phys., 7, 855–874, 2007b,http://www.atmos-chem-phys.net/7/855/2007/.

Baklanov, A. and U. Korsholm: On-line integrated meteorologicaland chemical transport modelling: advantages and prospectives,Preprints ITM 2007: the 29th NATA/SPS International Techni-cal Meeting on Air Pollution Modelling and its Application, 24–28 September 2007, University of Aveiro, Portugal, 21–34, 2007.

Bao, J.-W., Michelson, S. A., McKeen, S. A., and Grell, G. A.: Me-teorological evaluation of a weather-chemistry forecasting modelusing observations from the TEXAS AQS 2000 field experiment,J. Geophys. Res., 110, D21105, doi:10.1029/2004JD005024,2005.

Barrie, L. A., Yi, Y., Leaitch, W. R., Lohmann, U., Kasibhatla, P.,Roelofs, G.-J., Wilson, J., McGovern, F., Benkovitz, C., Me-lieres, M. A., Law, K., Prospero, J., Kritz, M., Bergmann, D.,Bridgeman, C., Chin, M., Christensen, J., Easter, R., Feichter, J.,Jeuken, A., Kjellstrom, E., Koch, D., Land, C., and Rasch, P.:A comparison of large scale atmospheric sulphate aerosol mod-els (COSAM): Overview and highlights, Tellus, 53B, 615–645,2001.

Barth, M. C., Rasch, P. J., Kiehl, J. T., Benkovitz, C. M., andSchwartz, S. E.: Sulfur chemistry in the National Center for At-mospheric Research Community Climate Model: Description,evaluation, features, and sensitivity to aqueous chemistry, J. Geo-phys. Res., 105(D1), 1387–1416, doi:10.1029/1999JD900773,2000.

Bazhin, N. M., Penenko, V. V., Aloyan, A. E., and Skubnevskaya,G. N.: Mathematical modelling of the distribution of atmosphericcontaminants taking account of chemical transformations, Russ.Chem. Rev., 60(3), 244–248, 1991.

Beagley, S. R., de Grandpre, J., Koshyk, J. N., McFarlane, N. A.,and Shepherd, T. G.: Radiativedynamical climatology of thefirst-generation Canadian Middle Atmosphere Model, Atmos.Ocean, 35, 293–331, 1997.

Binkowski, F.S. and Shankar, U.: The regional particulate mattermodel, 1. Model description and preliminary results, J. Geophys.Res., 100, 26 191–26 209, 1995.

Binkowski, F. S. and Roselle, S. J.: Models-3 Communitymultiscale air quality (CMAQ) model aerosol component,1. Model description, J. Geophys. Res., 108(D6), 4183,doi:10.1029/2001JD001409, 2003.

Boucher O. and Pham, M.: History of sulfate aerosol


http://www.atmos-chem-phys.net/3/1/2003/

http://www.cost728.org



radiative forcings, Geophys. Res. Lett., 29(9), 1308,doi:10.1029/2001GL014048, 2002.

Boville, B. A., Rasch, P. J., Hack, J. J., and McCaa, J. R.: Repre-sentation of Clouds and Precipitation Processes in the Commu-nity Atmosphere Model Version 3 (CAM3), J. Climate, 19(11),2184–2198, 2006.

Braham, R. R. Jr.: An Archaeal Iron-Oxidizing Extreme AcidophileImportant in Acid Mine Drainage, Meteorol. Monogr., 18, 141,1981.

Brasseur, G. P. and Roeckner, E.: Impact of improved air quality onthe future evolution of climate, Geophys. Res. Lett., 32, L23704,doi:10.1029/2005GL023902, 2005.

Byun, D. W. and Ching, J. K. S.: Science Algorithms of the EPAModels-3 Community Multiscale Air Quality [CMAQ] Mod-eling System, EPA/600/R-99/030, Office of Research and De-velopment, U.S. Environmental Protection Agency, Washington,D.C., 1999.

Cariolle, D. and Deque, M.: Southern hemisphere medium-scalewaves and total ozone disturbances in a spectral general circula-tion model, J. Geophys. Res., 91, 10 825–10 846, 1986.

Cariolle, D., Lasserre-Bigorry, A., Royer, J.-F., and Geleyn, J.-F.: Ageneral circulation model simulation of the springtime Antarcticozone decrease and its impact on mid-latitudes, J. Geophys. Res.,95(D2), 1883–1898, doi:10.1029/89JD01644, 1990.

Cerveny R. S. and Bailing Jr., R. C.: Weekly cycles of air pollutants,precipitation and tropical cyclones in the coastal NW Atlantic re-gion, Nature, 394, 561–563, 1998.

Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Ghan, S. J.,Kiehl, J. T., Le Treut, H., Li, Z.-X., Liang, X.-Z., Mitchell, J.F. B., Morcrette, J.-J., Randall, D. A., Riches, M. R., Roeckner,E., Schlese, U., Slingo, A., Taylor, K. E., Washington, W. M.,Wetherald, R. T., and Yagai, I.: Interpretation of Cloud-ClimateFeedback as Produced by 14 Atmospheric General CirculationModels, Science, 245(4917), 513–516, 1989.

Cess, R. D., Potter, G. L., Ghan, S.J., and Gates, W. L.: The cli-matic effects of large injections of atmospheric smoke and dust:A study of climate feedback mechanisms with one- and three-dimensional models, J. Geophys. Res., 90, 12 937–12 950, 1985.

Chang, J. S., Binkowski, F. S., Seaman, N. L., McHenry, J. N.,Samson, P. J., Stockwell, W. R., Walcek, C. J., Madronich, S.,Middleton, P. B., Pleim, J. E., and Lansford, H. H.: The re-gional acid deposition model and engineering model. State-of-Science/Technology, Report 4, National Acid Precipitation As-sessment Program, Washington, DC, 1989.

Chen, S.-H., and Sun, W.-Y.: A one-dimensional time dependentcloud model, J. Meteor. Soc. Japan, 80, 99-118, 2002.

Chenevez, J., Baklanov, A., and Sørensen, J. H.: Pollutant trans-port schemes integrated in a numerical weather prediction model:Model description and verification results, Meteorol. Appl.,11(3), 265–275, 2004.

Chou, C., Neelin, J. D., Tu, J.-Y. and Chen, C.-T.: Regional tropi-cal precipitation change mechanisms in ECHAM4/OPYC3 underglobal warming, J. Climate, 19, 4207–4223, 2006.

Chou, M. D. and Suarez, M. J.: An efficient thermal infrared ra-diation parameterization for use in general circulation models,NASA Tech. Memo., 104606, 3, 85 pp., 1994.

Chuang, C. C., Penner, J. E., Taylor, K. E., Grossman, A. S., andWalton, J. J.: An assessment of the radiative effects of anthro-pogenic sulfate, J. Geophys. Res., 102, 3761–3778, 1997.

Chuang, C. C., Penner, J. E., Prospero, J. M., Grant, K. E.,Rau, G. H., and Kawamoto, K.: Cloud susceptibility andthe first aerosol indirect forcing: Sensitivity to black carbonand aerosol concentrations, J. Geophys. Res., 107(D21), 4564,doi:10.1029/2000JD000215, 2002.

Chung, S. H. and Seinfeld, J. H.: Global distribution and climateforcing of carbonaceous aerosols, J. Geophys. Res., 107(D19),4407, doi:10.1029/2001JD001397, 2002.

Chung, S. H. and Seinfeld, J. H.: Climate response of direct ra-diative forcing of anthropogenic black carbon, J. Geophys. Res.,110(D1), 1102, doi:10.1029/2004JD005441, 2005.

Clark, J. H. E.: A quasi-geostrophic model of the winter strato-spheric circulation, Mon. Weather Rev., 98(6), 443–461, 1970.

Cunnold, D., Alyea, F., Phillips, N., and Prinn, R.: A three-dimensional dynamical-chemical model of atmospheric ozone,J. Atmos. Sci., 32, 170–194, 1975.

Collins, W. D. Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J.R., Williamson, D. L., Kiehl, J. T., and Briegleb, B.: Descriptionof the NCAR Community Atmosphere Model (CAM3), Tech.Rep. NCAR/TN-464STR, National Center for Atmospheric Re-search, Boulder, CO, 226 pp., 2004.

Collins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J.R., Williamson, D. L., and Briegleb, B. P.: The Formulation andAtmospheric Simulation of the Community Atmosphere Model,Version 3 (CAM3), J. Climate, 19, 2144–2161, 2006a.

Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B.,Bretherton, C. S., Carton, J. A., Chang, P., Doney, S. C., Hack, J.J., Henderson, T. B., Kiehl, J. T., Large, W. G., McKenna, D. S.,Santer, B. D., and Smith, R. D.: The Community Climate SystemModel version 3 (CCSM3), J. Climate, 19, 2122–2143, 2006b.

Covey, C., Schneider, S. H., and Thompson, S. L.: Global atmo-spheric effects of massive smoke injections from a nuclear war:results from general circulation model simulations, Nature, 308,21–25, 1984.

Cox, P. M.: Description of the TRIFFID dynamic global vegetationmodel, Tech. Note 24, Hadley Centre, Met Office, 16 pp., 2001.

Cote, J., Gravel, S., Methot, A., Patoine, A., Roch, M., and Stan-iforth, A.: The operational CMC MRB Global EnvironmentalMultiscale (GEM) model: Part I – Design considerations andformulation, Mon. Weather Rev., 126, 1373–1395, 1998a.

de Laat, A. T. J., Zachariasse, M., Roelofs, G. J., van Velthoven, P.,Dickerson, R. R., Rohoads, K. P., Oltmans, S. J., and Lelieveld,J.: Tropospheric O3 distribution over the Indian Ocean duringspring 1995 evaluated with a chemistry-climate model, J. Geo-phys. Res., 104(D11), 13, 881–13 893, 1999.

Dameris, M., Grewe, V., Hein, R., and Schnadt, C.: Assessment offuture development of the ozone layer, Geophys. Res. Lett., 25,3579–3582, 1998.

Dameris, M., Grewe, V., Ponater, M., Deckert, R., Eyring, V.,Mager, F., Matthes, S., Schnadt, C., Stenke, A., Steil, B., Bruhl,C., and Giorgetta, M. A. : Long-term changes and variability in atransient simulation with a chemistry-climate model employingrealistic forcings, Atmos. Chem. Phys., 5, 2121–2145, 2005,http://www.atmos-chem-phys.net/5/2121/2005/.

Del Genio, A. D., Yao, M.-S., Kovari, W., and Lo, K. K.-W.: Aprognostic cloud water parameterization for global climate mod-els, J. Climate, 9, 270–304, 1996.

Derwent, R. G., Collins, W. J., Jenkin, M. E., Johnson, C. E., andStevenson, D. S.: The global distribution of secondary partic-




ulate matter in a 3-D Lagrangian chemistry transport model, J.Atmos. Chem., 44, 57–95, 2003.

Doney, S. C., Lindsay, K., Fung, I., and John, J.: Natural variabilityin a stable, 1000-yr global coupled climate–carbon cycle simula-tion, J. Climate, 19, 3033–3054, 2006.

Dudia, J.: Numerical study of convection observed during thewinter monsoon experiment using a mesoscale two-dimensionalmodel, J. Atmos. Sci., 46, 3077–3107, 1989.

Eagen, R. C., Hobbs, P. V., and Radke, L. F.: Particle emissionsfrom a large Kraft paper mill and their effects on the microstruc-ture of warm clouds, J. Appl. Meteor., 13, 535–552, 1974.

Easter, R. C., Ghan, S. J., Zhang, Y., Saylor, R. D., Chapman, E.G., Laulainen, N. S., Abdul-Razzak, H., Leung, L. R., Bian, X.,and Zaveri, R. A.: MIRAGE: Model description and evaluationof aerosols and trace gases, J. Geophys. Res., 109(D2), 0210,doi:10.1029/2004JD004571, 2004.

Eckman, R. S., Grose, W. L., Turner, R. E., and Blackshear, W. T.:Polar ozone depletion: A three-dimensional chemical modelingstudy of its long-term global impact, J. Geophys. Res., 101(D17),22 977–22 990, doi:10.1029/96JD02130, 1996.

Eder, B. K., Kang, D., Stein, A. F., Grell, G. A., Peckham, S. E.,and McHenry, J.: The New England Air Quality Forecasting Pi-lot Program: Development of an Evaluation Protocol and Perfor-mance Benchmark, J. Air and Waste Manag., 55, 20–27, 2005.

Eyring, V., Harris, N. R. P., Rex, M., Shepherd, T. G., Fahey, D. W.,Amanatidis, G. T., Austin, J., Chipperfield, M. P., Dameris, M.,De F. Forster, P. M., Gettelman, A., Graf, H. F., Nagashima, T.,Newman, P. A., Pawson, S., Prather, M. J., Pyle, J. A., Salawitch,R. J., Santer, B. D., and Waugh, D. W.: A strategy for process-oriented validation of coupled chemistry-climate models, B.Am. Meteorol. Soc., 86(8), 1117–1133, doi:10.1175/BAMS-868-1117, 2005.

Fast, J. D.: Evaluation of the boundary layer characteristics andpollutants in Mexico City predicted by WRF: Presented at the6th Annual WRF/15th Annual MM5 Users’ Workshop, Boulder,CO, 2005.

Fast, J. D., Gustafson Jr., W. I., Easter, R. C., Zaveri, R. A., Barnard,J. C., Chapman, E. G., and Grell, G. A.: Evolution of ozone,particulates, and aerosol direct forcing in an urban area using anew fully-coupled meteorology, chemistry, and aerosol model, J.Geophys. Res., 111, D21305, doi:10.1029/2005JD006721, 2006.

Feichter, J., Kjellstrom, E., Rodhe, H., Dentener, F., Lelieveld, J.,and Roelofs, G.-J.: Simulation of the tropospheric sulfur cycle ina global climate model, Atmos. Environ., 30, 1693–1707, 1996.

Feichter, J., Lohmann, U., and Schult, I.: The atmospheric sulfurcycle in ECHAM-4 and its impact on the shortwave radiation,Clim. Dynam., 13, 235–246, 1997.

Feichter J., Sausen, R., Graßl, H., and Fiebig, M.: Comment on“Control of fossil-fuel particulate black carbon and organic mat-ter, possibly the most effective method of slowing global warm-ing”, by Jacobson, M. Z., J. Geophys. Res., 108(D24), 4767,doi:10.1029/2002JD003223, 2003.

Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W.,Brovkin, V., Cadule, P., Doney, S., Eby, M., Fung, I., Bala, B.,John, J., Jones, C., Joos, F., Kato, T., Kawamiya, M., Knorr, W.,Lindsay, K., Matthews, H. D., Raddatz, T., Rayner, P., Reick,C., Roeckner, E., Schnitzler, K.-G., Schnurr, R., Strassmann, K.,Weaver, A. J., Yoshikawa, C., and Zeng, N.: Climate–CarbonCycle Feedback Analysis: Results from the C4MIP Model Inter-

comparison, J. Climate, 19, 3337–3353, 2006.Frost G. J., McKeen, S. A., Trainer, M., Ryerson, T. B., Neuman, J.

A., Roberts, J. M., Swanson, A., Holloway, J. S., Sueper, D. T.,Fortin, T., Parrish, D. D., Fehsenfeld, F. C., Flocke,F., Peckham,S. E., Grell, G. A., Kowal, D., Cartwright, J., Auerbach, N., andHabermann, T.: Effects of changing power plant NOx emissionson ozone in the eastern United States: Proof of concept, J. Geo-phys. Res., 111, D12306, doi:10.1029/2005JD006354, 2006.

Gery, M. W., Whitten, G. Z., Killus, J. P., and Dodge, M. C.: Aphotochemical kinetics mechanism for urban and regional scalecomputer modeling, J. Geophys. Res., 94, 12 925–12 956, 1989.

Ghan, S. J.: Sensitivity of Indirect Effect to Aerosol Treatment,presentation at the 12th Annual CCSM Workshop, 19–21 June,Breckenridge, CO., 2007.

Ghan, S., and Easter, R.: Impact of cloud-borne aerosol representa-tion on aerosol direct and indirect effects, Atm. Chem. Phys., 6,4163–4174, 2006.

Ghan, S. J., Laulainen, N. S., Easter, R. C., Wagener, R., Nemesure,S., Chapman, E. G., Zhang, Y., and Leung, L. R.: Evaluation ofaerosol direct radiative forcing in MIRAGE, J. Geophys. Res.,106, 5295–5316, 2001a.

Ghan, S. J., MacCracken, M. C., and Walton, J. J.: Climatic re-sponse to large atmospheric smoke injections: sensitivity studieswith a tropospheric general circulation model, J. Geophys. Res.,106, 5295–5316, 1988.

Ghan, S. J., Leung, L. R., Easter, R. C., and Abdul-Razzak,H.: Prediction of cloud droplet number in a general cir-culation model, J. Geophys. Res., 102(D18), 21 777–21 794,doi:10.1029/97JD01810, 1997a.

Ghan, S. J., Ruby Leung, L., and Hu, Q.: Application of cloudmicrophysics to NCAR community climate model, J. Geophys.Res., 102(D14), 16 507–16 528, doi:10.1029/97JD00703, 1997b.

Ghan, S. J., Ruby Leung, L., and Hu, Q.: Application of cloudmicrophysics to NCAR community climate model, J. Geophys.Res., 102(D14), 16507–16528, doi:10.1029/97JD00703, 1997b.

Ghan, S. J., Easter, R. C., Hudson, J., and Breon, F.-M.: Evalu-ation of aerosol indirect radiative forcing in MIRAGE, J. Geo-phys. Res., 106, 5317–5334, 2001b.

Ghan, S. J., Easter, R. C., Chapman, E. G., Abdul-Razzak, H.,Zhang, Y., Leung, L. R., Laulainen, N. S., Saylor, R. D., andZaveri, R. A.: A physically-based estimate of radiative forcingby anthropogenic sulfate aerosol, J. Geophys. Res., 106, 5279–5293, 2001c.

Ghan, S. J. and Schwartz, S. E.: Aerosol properties and processes,a path from field and laboratory measurements to global climatemodels, B. Am. Meteorol. Soc., 88(7), 1059–1083, 2007.

Giorgi, F. and Shields, C.: Tests of precipitation parameterizationsavailable in the latest version of the NCAR regional climatemodel (RegCM) over continental U.S., J. Geophys. Res., 104,6353–6375, 1999.

Giorgi, F., Marinucci, M. R., and Bates, G. T.: Developmentof a second generation regional climate model(RegCM2), I,Boundary-layer and radiative transfer processes, Mon. WeatherRev., 121, 2794–2813, 1993a.

Giorgi, F., Marinucci, M. R., Bates, G. T., and De Canio,G.: Development of a second generation regional climatemodel(RegCM2), II, Convective processes and assimilation oflateral boundary conditions, Mon. Weather Rev., 121, 2814–2832, 1993b.



Giorgi, F., Bi, X., and Qian, Y.,: Direct radiative forcing and re-gional climatic effects of anthropogenic aerosols over East Asia:A regional coupled climate-chemistry/aerosol model study, J.Geophys. Res., 107(D20), 4439, doi:10.1029/2001JD001066,2002.

Givati, A. and Rosenfeld, D.: Quantifying Precipitation Suppres-sion due to air pollution, J. Appl. Meteor., 43, 1038–1056, 2004.

Givati, A. and Rosenfeld, D.: Separation between Cloud-Seedingand Air-Pollution Effects, J. Appl. Meteor., 44, 1298–1314,2005.

Gong, S. L., Barrie, L. A., and Lazare, M.: Canadian Aerosol Mod-ule (CAM): A size-segregated simulation of atmospheric aerosolprocesses for climate and air quality models, 2. Global sea-salt aerosol and its budgets, J. Geophys. Res., 107(D24), 4779,doi:10.1029/2001JD002004, 2002.

Gong, S. L., Barrie, L. A., Blanchet, J.-P., von Salzen, K., Lohmann,U., Lesins, G., Spacek, L., Zhang, L. M., Girard, E., Lin,H., Leaitch, R., Leighton, H., Chylek, P., and Huang, P.:Canadian Aerosol Module: A size-segregated simulation of at-mospheric aerosol processes for climate and air quality mod-els: 1. Model development, J. Geophys. Res., 108(D1), 4007,doi:10.1029/2001JD002002, 2003.

Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M.,Johns, T. C., Mitchell, J. F. B., and Wood, R. A.: The simula-tion of SST, sea ice extents and ocean heat transports in a versionof the Hadley Centre coupled model without flux adjustments,Clim. Dynam., 16, 147–168, 2000.

Gustafson, W. I., Chapman, E. G., Ghan, S. J., Easter, R. C.,and Fast, J. D.: Impact on modeled cloud characteristicsdue to simplified treatment of uniform cloud condensation nu-clei during NEAQS 2004, Geophys. Res. Lett., 34, L19809,doi:10.1029/2007GL0300321, 2007.

Granier, C. and Brasseur, G.: Ozone and other trace gasesin the Arctic and Antarctic regions: Three-dimensionalmodel simulations, J. Geophys. Res., 96(D2), 2995–3011,doi:10.1029/90JD01779, 1991.

Grell, G. A., Dudhia, J., and Stauffer, D.: A description of the fifth-generation Penn State/NCAR Mesocale Model (MM5), NCARTechnical Note, TN-398+STR, National Center for AtmosphericResearch, Boulder, CO, 138 pp., 1994.

Grell, G. A., Emeis, S., Stockwell, W. R., Schoenemeyer, T., Forkel,R., Michalakes, J., Knoche, R., and Seidl, W.: Application of amultiscale, coupled MM5/chemistry model to the complex ter-rain of the VOTALP valley campaign, Atmos. Environ. 34, 1435–1453, 2000.

Grell, G. A., McKeen, S., Michalakes, J., Bao, J.-W., Trainer, M.,and Hsie, E.-Y.: Real-time simultaneous prediction of air pollu-tion and weather during the Houston 2000 Field Experiment, pre-sented at the 4th Conference on Atmospheric Chemistry: Atmo-spheric Chemistry and Texas Field Study, 13–17 January, Amer-ican Meteorological Society, Orlando, 2002.

Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G.,Skamarock, W. C., and Eder, B.: Fully coupled “online” chem-istry within the WRF model, Atmos. Environ., 39, 6957–6975,2005.

Grenfell, J. L., Shindell, D. T., Koch, D., and Rind, D.: Chemistry-climate interactions in the Goddard Institute for Space Studiesgeneral circulation model 2, New insights into modeling thepreindustrial atmosphere, J. Geophys. Res., 106(D24), 33 435–

33 452, doi:10.1029/2000JD000090, 2001.Griffin, R. J., Cocker III, D. R., Flagan, R. C., and Seinfeld, J. H.:

Organic aerosol formation from the oxidation of biogenic hydro-carbons, J. Geophys. Res., 104, 3555–3567, 1999.

Griffin, R. J., Dabdub, D., Kleeman, M. J., Fraser, M.P., Cass, G. R., and Seinfeld, J. H.: Secondary OrganicAerosol: III. Urban/Regional Scale Model Of Size- AndComposition-Resolved Aerosols, J. Geophys. Res., 107(D17),4334, doi:10.1029/2001JD000544, 2002.

Guenther A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., andGeron, C.: Estimates of global terrestrial isoprene emissions us-ing MEGAN (Model of Emissions of Gases and Aerosols fromNature), Atmos. Chem. Phys., 6, 107-173, 2006,http://www.atmos-chem-phys.net/6/107/2006/.

Hack, J. J., Boville, B. A., Briegleb, B. P., Kiehl, J. T., Rasch, P.J., and Williamson, D. L.: Description of the NCAR Commu-nity Climate Model (CCM2), Tech. Rep. NCAR/TN382STR,National Center for Atmospheric Research, 120 pp., 1993.

Hack, J. J., Caron, J. M., Yeager, S. G., Oleson, K. W., Holland, M.M., Truesdale, J. E., and Rasch, P. J.: Simulation of the globalhydrological cycle in the CCSM Community Atmosphere Modelversion 3 (CAM3): Mean features, J. Climate, 19, 2199–2221,2006.

Harrington, D. Y. and Kreidenweis, S. M.: Simulation of SulfateAerosol Dynamics, I. Model Description, Atmos. Environ., 32,1691–1700, 1998.

Hauglustaine, D. A., Hourdin, F., Jourdain, L., Filiberti, M.-A.,Walters, S., Lamarque, J.-F., and Holland, E. A.: Interac-tive chemistry in the Laboratoire de Meteorologie Dynamiquegeneral circulation model: Description and background tropo-spheric chemistry evaluation, J. Geophys. Res., 109, D04314,doi:10.1029/2003JD003957, 2004.

Heald, C. L.: Update on CAM/CLM Biogenic VOC Emis-sions and Secondary Organic Aerosols, presentation at theChemistry-Climate Working Group Meeting, CCSM, February,2007.Henze, D.K. and J.H. Seinfeld: Global secondary organicaerosol formation from isoprene oxidation, Geophys. Res. Lett.,33, L09812, doi:1029/2006GL025976, 2006.

Hogrefe, C., Lynn, B., Civerolo, K., Ku, J.-Y., Rosenthal, J., Rosen-zweig, C., Goldberg, R., Gaffin, S., Knowlton, K., and Kin-ney, P. L.: Simulating changes in regional air pollution overthe eastern United States due to changes in global and re-gional climate and emissions, J. Geophys. Res., 109(D2), 2301,doi:10.1029/2004JD004690, 2004.

Hong, S.-Y., Lim, K.-S., Kim, J.-H., Lim,J.-O. J., and Dudhia, J.:WRF Single-Moment 6-Class Microphysics Scheme (WSM6),presented at the 7th Annual WRF Users Workshop, 19–22 June,Boulder, CO, 2006.

Hu, X.-M. and Zhang, Y.: Implementation and testing of a newaerosol module in WRF/Chem, presentation at the 86th AMSAnnual Meeting/the 8th Conference on atmospheric chemistry,Atlanta, GA, 27 January–3 February, 2006.

Hu, X.-M. and Zhang, Y.: Gas/Particle Mass Transfer Treatmentsin WRF/Chem-MADRID: Development, Application, and Eval-uation, presentation at the 8th Annual WRF User’s Workshop,11–15 June, Boulder, CO., 2007.

Hu, X.-M., Frazier, E. F., Zhang, Y., McKeen, S. A., Grell, G.A., Peckham, S. E., Yu, S.-C., and Schere, K. L.: Evaluationof WRF/Chem-MADRID with the July 2004 New England Air




Quality Study Episode, to be presented at the 6th Annual CMASModels-3 User’s Conference, 1–3 October 2007, Chapel Hill,NC., 2007.

Hu, X.-M., Zhang, Y., Jacobson, M. Z., and Chan, C. K.: Cou-pling and Evaluating Gas/Particle Mass Transfer Treatments forAerosol Simulation and Forecast, J. Geophys. Res., in press,2008.

Huang, J.-P., Hu, X.-M., Zhang, Y., Sarwar, G., Otte, T. L., Gilliam,R., and Schere, K. L.: Implementation and Testing of the 2005Version of Carbon Bond Mechanism in WRF-chem, presenta-tion at the 7th Annual WRF User’s Workshop, Boulder, CO, 19–22 June, 2006.

Hunt, B. G.: Experiments with a stratospheric general circulationmodel III. Large-scale diffusion of ozone including photochem-istry, Mon. Weather Rev., 97(4), 287–306, 1969.

Hurrell, J. W., Hack, J. J., Phillips, A., Caron, J., and Yin, J.: Thedynamical simulation of the Community Atmospheric ModelVersion 3 (CAM3), J. Climate, 19, 2162–2183, 2006.

Intergovernmental Panel on Climate Change (IPCC): ClimateChange 2001, The Scientific Basis, Contribution of WorkingGroup I to the Third Assessment Report of the IngovernmentalPanel on Climate Change, edited by: Houghton, J. T., Ding, Y.,Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell,K., and Johnson, C. A., 944 pp., Cambridge Univ. Press, NewYork, 2001.

Intergovernmental Panel on Climate Change (IPCC): ClimateChange 2007, http://www.ipcc.ch/, 2007.

Iversen, T. and Seland, Ø.: A scheme for process-tagged SO4and BC aerosols in NCAR CCM3: Validation and sensitiv-ity to cloud processes, J. Geophys. Res., 107(D24), 4751,doi:10.1029/2001JD000885, 2002.

Jacobson, M. Z.: Developing, coupling, and applying a gas, aerosol,transport, and radiation model to study urban and regional airpollution, Ph. D. Thesis, Department of Atmospheric Sciences,University of California, Los Angeles, 436 pp., 1994.

Jacobson, M. Z.: Simulations of the rates of regeneration ofthe global ozone layer upon reduction or removal of ozone-destroying compounds, EOS Supplement, Fall, 1995, p. F119.“Closing the hole,” Geotimes Magazine (American GeologicalInstitute), April 1996, 9, 1995.

Jacobson, M. Z., Lu, R., Turco, R. P., and Toon, O. B.: Developmentand application of a new air pollution modeling system. Part I:Gas-phase simulations, Atmos. Environ., 30B, 1939–1963, 1996.

Jacobson, M. Z.: Development and application of a new air pollu-tion modeling system – II. Aerosol module structure and design,Atmos. Environ., 31, 131–144, 1997a.

Jacobson, M. Z.: Development and application of a new air pollu-tion modeling system, Part III: Aerosol-phase simulations, At-mos. Environ., 31A, 587–608, 1997b.

Jacobson, M. Z.: Testing the impact of interactively coupling a me-teorological model to an air quality model, in: Measurementsand Modeling in Environmental Pollution, edited by: San Jose,R. and Brebbia, C. A., Computational Mechanics Publications,Southampton, 241–249, 1997c.

Jacobson, M. Z.: Studying the effects of aerosols on vertical pho-tolysis rate coefficient and temperature profiles over an urban air-shed, J. Geophys. Res., 103, 10 593–10 604, 1998.

Jacobson, M. Z.: Isolating nitrated and aromatic aerosols and ni-trated aromatic gases as sources of ultraviolet light absorption, J.

Geophys. Res., 104, 3527–3542, 1999a.Jacobson, M. Z.: Studying the effects of soil moisture on ozone,

temperatures, and winds in Los Angeles, J. Appl. Meteorol., 38,607–616, 1999b.

Jacobson, M. Z.: Studying the effects of calcium and magnesiumon size-distributed nitrate and ammonium with EQUISOLV II,Atmos. Environ., 33, 3635–3649, 1999c.

Jacobson, M. Z.: A physically-based treatment of elemental carbonoptics: Implications for global direct forcing of aerosols, Geo-phys. Res. Lett., 27, 217–220, 2000.

Jacobson, M. Z.: Strong radiative heating due to the mixing stateof black carbon in atmospheric aerosols, Nature, 409, 695–697,2001a.

Jacobson, M. Z.: Global direct radiative forcing due to multicompo-nent anthropogenic and natural aerosols, J. Geophys. Res., 106,1551–1568, 2001b.

Jacobson, M. Z.: GATOR-GCMM: A global- through urban-scaleair pollution and weather forecast model 1. Model design andtreatment of subgrid soil, vegetation, roads, rooftops, water, sea,ice, and snow, J. Geophys. Res., 106, 5385–5401, 2001c.

Jacobson, M. Z.: GATOR-GCMM: 2. A study of day- and night-time ozone layers aloft, ozone in national parks, and weatherduring the SARMAP Field Campaign, J. Geophys. Res., 106,5403–5420, 2001d.

Jacobson, M. Z.: Control of fossil-fuel particulate black car-bon plus organic matter, possibly the most effective method ofslowing global warming, J. Geophys. Res., 107(D19), 4410,doi:10.1029/2001JD001376, 2002.

Jacobson, M. Z.: Reply to comment by: J. Feichter et al. on Con-trol of fossil-fuel particulate black carbon and organic matter,possibly the most effective method of slowing global warming,J. Geophys. Res., 108(D24), 4768, doi:10.1029/2002JD003299,2003a.

Jacobson, M. Z.: Reply to comment by: J. E. Penner on Control offossil&hyphen;fuel particulate black carbon and organic matter,possibly the most effective method of slowing global warming,J. Geophys. Res., 108(D24), 4772, doi:10.1029/2003JD003403,2003b.

Jacobson, M. Z.: Development of mixed-phase clouds frommultiple aerosol size distributions and the effect of theclouds on aerosol removal, J. Geophys. Res., 108(D8), 4245,doi:10.1029/2002JD002691, 2003c.

Jacobson, M. Z.: The short-term cooling but long-term globalwarming due to biomass burning, J. Climate, 17, 2909–2926,2004a.

Jacobson, M. Z.: The climate response of fossil-fuel and bio-fuel soot, accounting for soot’s feedback to snow and seaice albedo and emissivity, J. Geophys. Res., 109, D21201,doi:10.1029/2004JD004945, 2004b.

Jacobson, M. Z. and Seinfeld, J. H.: Evolution of nanoparticle sizeand mixing state near the point of emission, Atmos. Environ., 38,1839–1850, 2004.

Jacobson, M. Z., Seinfeld, J. H., Carmichael, G. R., and Streets,D. G.: The effect on photochemical smog of converting the U.S.fleet of gasoline vehicles to modern diesel vehicles, Geophys.Res. Lett., 31, L02116, doi:10.1029/2003GL018448, 2004.

Jacobson, M. Z.: A solution to the problem of nonequilibriumacid/base gas-particle transfer at long time step, Aerosol Sci.Technol., 39, 92–103, 2005a.


http://www.ipcc.ch/


Jacobson, M. Z.: Studying ocean acidification with conserva-tive, stable numerical schemes for nonequilibrium air-ocean ex-change and ocean equilibrium chemistry, J. Geophys. Res., 110,D07302, doi:10.1029/2004JD005220, 2005b.

Jacobson, M. Z.: Effects of absorption by soot inclusions withinclouds and precipitation on global climate, J. Phys. Chem., 110,6860–6873, 2006a.

Jacobson, M. Z.: Comment on “Fully coupled “online” chemistrywithin the WRF model,” by Grell et al., Atmos. Environ., 39,6957–6975, 2006b.

Jacobson, M. Z.: Effects of ethanol (E85) versus gasoline vehicleson cancer and mortality in the United States, Environ. Sci. Tech-nol., 41(11), 4150–4157, doi:10.1021/es062085v, 2007.

Jacobson, M. Z. and Kaufmann, Y. J.: Aerosol reduc-tion of the wind, Geophys. Res. Lett., 33, L24814,doi:10.1029/2006GL027838, 2006.

Jacobson, M. Z., Kaufmann, Y. J., and Rudich, Y.: Examining feed-backs of aerosols to urban climate with a model that treats 3-Dclouds with aerosol inclusions, J. Geophys. Res., 112, D24205,doi:10.1029/2007JD008922, 2007a.

Jaecker-Voirol, A. and Mirabel, P.: Heteromolecular Nucleation inthe Sulfuric Acid-Water System, Atmos. Environ., 23, 2033–2057, 1989.

Jauregui E. and Romales, E.: Urban effects on convective precipi-tation in Mexico city, Atmos. Environ., 30, 3383–3389, 1996.

Johnson, C. E., Collins, W. J., Stevenson, D. S., and Derwent, R. G.:Relative roles of climate and emissions changes on future tropo-spheric oxidant concentrations, J. Geophys. Res., 104, 18 631–18 645, 1999.

Johnson, C. E., Stevenson, D. S., Collins, W. J., and Derwent, R.G.: Role of climate feedback on methane and ozone studied witha coupled Ocean-Atmosphere-Chemistry model, Geophys. Res.Lett., 28(9), 1723–1726, 2001.

Jockel, P., Sander, R., Kerkweg, A., Tost, H., and Lelieveld, J.:Technical note: the modular earth submodel system (MESSy)– a new approach towards earth system modeling, Atmos. Chem.Phys., 5, 433–444, 2005,http://www.atmos-chem-phys.net/5/433/2005/.

Jockel P., Tost, H., Pozzer, A., Bruhl, C., Buchholz, J., Ganzeveld,L., Hoor, P., Kerkweg, A., Lawrence, M. G., Sander, R., Steil,B., Stiller, G., Tanarhte, M., Taraborrelli, D., van Aardenne, J.,and Lelieveld, J.: The atmospheric chemistry general circulationmodel ECHAM5/MESSy1: consistent simulation of ozone fromthe surface to the mesosphere, Atmos. Chem. Phys., 6, 5067–5104, 2006,http://www.atmos-chem-phys.net/6/5067/2006/.

Joseph, J. H.: The effect of a desert aerosol on a model of the gen-eral circulation, Proc. Symp.: in: On Radiation in the Atmo-sphere, edited by: Bolle, H. J., Science Press, 487–492, 1976.

Jungclaus, J. H., Keenlyside, N., Botzet, M., Haak, H., Luo, J.-J., Latif, M., Marotzke, J., Mikolajewicz, U., and Roeckner,E.:Ocean circulation and tropical variability in the coupled modelECHAM5/MPIOM., J. Climate, 19, 3952–3972, 2006.

Kaminski, J.: Multiscale Atmospheric Chemistry Modelling withGEM-AQ, presentation at the COST-728 / NetFAM workshop onIntegrated systems of meso-meteorological and chemical trans-port models, Copenhagen, Denmark, 21–23 May, 2007.

Kang, D., Eder, B. K., Stein, A. F., Grell, G. A., Peckham, S. E.,and McHenry, J.: The New England Air Quality Forecasting Pi-

lot Program: Development of an evaluation protocol and perfor-mance benchmark, J. Air Waste Manage. Assoc., 55, 1782–1796,2005.

Katrlnak, K. A., Rez, P., and Buseck, P. R.: Structural variations inindividual carbonaceous particles from an urban aerosol, Envi-ron. Sci. Technol., 26, 1967–1976, 1992.

Katrlnak, K. A., Rez, P., Perkes, P. R., and Buseck, P. R.: Frac-tal geometry of carbonaceous aggregates from an urban aerosol,Environ. Sci. Technol., 27, 539–547, 1993.

Kaufman, Y. J. and Fraser, R. S.: The effect of smoke parti-cles on clouds and climate forcing, Science, Washington, DC,277(5332), 1636–1638, 12 September, 1997.

Kessler, E.: On the distribution and continuity of water substancein atmospheric circulation. Meteor. Monogr., No. 32, Amer. Me-teor. Soc., 84 pp., 1969.

Khodri, M., Leclainche, Y., Ramstein, G., Braconnot, P., Marti, O.,and Cortijo, E.: Simulating the amplification of orbital forcingby ocean feedbacks in the last glaciation, Nature, 410, 570–574,2001.

Kiehl, J. T., Hack, J. J., Bonan, G. B., Boville, B. B., Williamson,D. L., and Rasch, P. J.: The National Center for AtmosphericResearch Community Climate Model: CCM3, J. Climate, 11,1131–1149, 1998.

Kiehl, J. T., Schneider, T. L., Rasch, P. J., Barth, M. C., and Wong,J.: Radiative forcing due to sulfate aerosols from simulationswith the NCAR community climate model, version 3, J. Geo-phys. Res., 105, 1441–1457, 2000.

Kiehl, J. T. and Gent, P. R.: The Community Climate SystemModel, version 2, J. Climate, 17, 3666–3682, 2004.

Kinne, S., Lohmann, U., Feichter, J., Schulz, M., Timmreck, C.,Ghan, S. J., Easter, R. C., Chin, M., Ginoux, P., Takemura, T.,Tegen, I., Koch, D., Herzog, M., Penner, J., Pitari, G., Hol-ben, B. N., Eck, T., Smirnov, A., Dubovik, O., Slutsker, I.,Tanre, D., Torres, O., Mishchenko, M., Geogdzhayev, I., Chu,D. A., and Kaufman, Y. J.: Monthly averages of aerosol prop-erties: A global comparison among models, satellite data andAERONET ground data, J. Geophys. Res., 108(D20), 4634,doi:10.1029/2001JD001253, 2003.

Kinne, S., Schulz, M., Textor, C., Guibert, S., Balkanski, Y., Bauer,S., Berntsen, T., Berglen, T., Boucher, O., Chin, M., Collins,W., Dentener, F., Diehl, T., Easter, R., Feichter, H., Fillmore, D.,Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Herzog,M., Horrowitz, L., Isaksen, I., Iversen, T., Kirkevag, A., Kloster,S., Koch, D., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque,J. F., Lesins, G., Liu, X., Lohmann, U., Montanaro, V., Myhre,G., Penner, J., Pitari, G., Reddy, S., Seland, O., Stier, P., Take-mura, T., and Tie, X.: An AeroCom initial assessment opticalproperties in aerosol component modules of global models, Atm.Chem. Phy., 6, 1815–1834, 2005.

Kim, D. and Stockwell, W. R.: An online-coupled meteorologi-cal and air quality modeling study of the effect of complex ter-rain on the regional transport and transformation of air pollutantsover the Western United States, Atmos. Environ., 41, 2319–2334,2007.

Knoth, O. and Wolke, R.: The online-coupled atmospheric-chemistry aerosol model LMMUSCAT, presentation at theCOST-728/NetFAM workshop on Integrated systems of me-someteorological and chemical transport models, Copenhagen,Denmark, 21–23 May, 2007.





Koch, D., Jacob, D., Tegen, I., Rind, D., and Chin, M.: Tro-pospheric sulfur simulation and sulfate direct radiative forcingin the Goddard Institute for Space Studies general circulationmodel, J. Geophys. Res., 104, 23 799–23 822, 1999.

Korsholm, U., Baklanov, A., Mahura, A., Gross, A., Sørensen, J. H.,Kaas, E., Chenevez, J., and Lindberg, K.: ENVIROHIRLAM:on-line integrated system, presentation at the COST728/NetFAMworkshop on Integrated systems of meso-meteorological andchemical transport models, Copenhagen, Denmark, 21–23 May,2007.

Kulmala, M., Laaksonen, A., and Pirjola, L.: Parameterizationsfor sulphric acid/water nucleation rates, J. Geophys. Res., 103,8301–8308, 1998.

Lamarque, J.-F., Kiehl, J. T., Hess, P. G., Collins, W. D., Em-mons, L. K., Ginoux, P., Luo, C., and Tie, X. X.: Response ofa coupled chemistry-climate model to changes in aerosol emis-sions: Global impact on the hydrological cycle and the tropo-spheric burdens of OH, ozone, and NOx, Geophys. Res. Lett.,32, L16809, doi:10.1029/2005GL023419, 2005.

Langmann, B.: Numerical modelling of regional scale transport andphotochemistry directly together with meteorological processes,Atmos. Environ. 34, 3585–3598, 2000.

Langmann, B.: A model study of the smoke-haze influ-ence on clouds and warm precipitation formation inIndonesia 1997/1998, Atmos. Environ. 41, 6838–6852,doi:10.1016/j.atmosenv.2007.04.050, 2007.

Langmann, B., Varghese, S., Marmer, E., Vignati, E., Wilson, J.,Stier, P., and O’Dowd, C.: Aerosol distribution over Europe: Amodel evaluation study with detailed aerosol microphysics, At-mos. Chem. Phys., 8, 1591–1607, 2008,http://www.atmos-chem-phys.net/8/1591/2008/.

Kusik, C. L. and Meissner, H. P.: Electrolyte activity coefficients ininorganic processing, AIChE Symp. Series, 173, 14–20, 1978.

Langner, J., Bergstrom, R., and Foltescu, V.: Impact of climatechange on surface ozone and deposition of sulphur and nitrogenin Europe, Atmos. Environ., 39, 1129–1141, 2005.

Levy, H. II, Mahiman, J. D., and Moxim, W. J.: Troposphericozone: the role of transport, J. Geophys. Res., 90(D2), 3753–3772, 1985.

Liao, H., Adams, P. J., Chung, S. H., Seinfeld, J. H., Mickley, L.J., and Jacob, D. J.: Interactions between tropospheric chemistryand aerosols in a unified general circulation model, J. Geophys.Res., 108(D1), 4001, doi:10.1029/2001JD001260, 2003.

Liao, H., Seinfeld, J. H., Adams, P. J., and Mickley, L. J.: Globalradiative forcing of coupled tropospheric ozone and aerosolsin a unified general circulation model, J. Geophys. Res., 109,D16207, doi:10.1029/2003JD004456, 2004.

Liao, H. and Seinfeld, J. H.: Global impacts of gas-phasechemistry-aerosol interactions on direct radiative forcing by an-thropogenic aerosols and ozone, J. Geophys. Res., 110(D1),8208, doi:10.1029/2005JD005907, 2005.

Liao, H., Chen, W.-T., and Seinfeld, J. H.: Role of climate changein global predictions of future tropospheric ozone and aerosols, J.Geophys. Res., 111, D12304, doi:10.1029/2005JD006852, 2006.

Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk parameterizationof the snow field in a cloud model, J. Climate Appl. Meteor., 22,1065–1092, 1983.

Lohmann, U. and Feichter, J.: Impact of sulfate aerosols on albedoand lifetime of clouds: A sensitivity study with the ECHAM

GCM, J. Geophys. Res., 102, 12 685–13 700, 1997.Lohmann, U., Feichter, J., Penner, J. E., and Leaitch, R.: Indirect

effect of sulfate and carbonaceous aerosols: A mechanistic treat-ment, J. Geophys. Res., 105, 12 193–12 206, 2000.

Lohmann, U., Stier, P., Hoose, C., Ferrachat, S., Kloster, S., Roeck-ner, E., and Zhang, J.: Cloud microphysics and aerosol indi-rect effects in the global climate model ECHAM5-HAM, Atmos.Chem. Phys., 7, 3425-3446, 2007,http://www.atmos-chem-phys.net/7/3425/2007/.

Madronich, S.: Photodissociation in the atmosphere: 1. Actinicflux and the effects of ground reflections and clouds, J. Geophys.Res., 92, 9740–9752, 1987.

Malone, R. C., Auer, L. H., Glatzmaier, G. A., Wood, M. C., Toon,O. B.: Nuclear winter: three-dimensional simulations includinginteractive transport, scavenging, and solar heating of smoke, J.Geophys. Res., 91, 1039–1054, 1986.

Manins, P.: Integrated modelling systems in Australia, CSIRO, pre-sentation at the COST-728 / NetFAM workshop on Integratedsystems of meso-meteorological and chemical transport models,Copenhagen, Denmark, 21–23 May, 2007.

Manzini, E., Steil, B., Bruhl, C., Giorgetta, M. A., and Kruger, K.:A new interactive chemistry-climate model: 2. Sensitivity of themiddle atmosphere to ozone depletion and increase in green-house gases and implications for recent stratospheric cooling,J. Geophys. Res., 108(D14), 4429, doi:10.1029/2002JD002977,2003.

Marchuk, G.I.: Matematicheskoe Modelirovanie v ProblemeOkruzhayushchei Sredy (Mathematical modeling in the environ-mental problems), Izd. Nauka, Moscow, 1982, 319 pp., 1982.

Mathur, R., Xiu, A., Coats, C., Alapaty, K., Shankar, U., and Hanna,A.: Development of an Air Quality Modeling System with Inte-grated Meteorology, Chemistry, and Emissions, Proc. Measure-ment of Toxic and Related Air Pollutants, AWMA, Cary, NC,September, 1998.

Maurizi, A.: BOLCHEM, an integrated system for meteorol-ogy and atmospheric composition, presentation at the COST-728/NetFAM workshop on Integrated systems of mesometeoro-logical and chemical transport models, Copenhagen, Denmark,21–23 May, 2007.

McKeen, S. A., Eder, B., Grell, G. A., McHenry, J., Stein, A., andAngevine, W. M.: Evaluation of prototype air quality forecastmodels—chemistry, NOAA/OAR report, 2003.

McKeen, S., Wilczak, J., Grell, G., Djalalova, I., Peckham, S., Hsie,E.-Y., Gong, W., Bouchet, V., Moffet, R., McHenry, J., Mc-Queen, J., Tang, Y., Carmichael, G. R., Pagowski, M., Chan,A., Dye, T., Frost, G., Lee, P., and Mathur, R.: Assessmentof an ensemble of seven real-time ozone forecasts over easternNorth America during the summer of 2004, J. Geophys. Res.,110, D21307, doi:10.1029/2005JD005858, 2005.

McKeen, S., Chung, S. H., Wilczak, J., Grell, G., Djalalova,I., Peckham, S., Gong, W., Bouchet, V., Moffet, R., Tang,Y., Carmichael, G. R., Mathur, R., and Yu, S.: Evaluationof several PM2.5 forecast models using data collected duringthe ICARTT/NEAQS 2004 field study, J. Geophys. Res., 112,D10S20, doi:10.1029/2006JD007608, 2007.

McMurry, P. H. and Friedlander, S. K.: New particle formationin the presence of an aerosol, Atmos. Environ., 13, 1635–1651,1979.

Menon, S., Del Genio, A. D., Koch, D., and Tselioudis, G.: GCM





Simulations of the Aerosol Indirect Effect: Sensitivity to CloudParameterization and Aerosol Burden, J. Atmos. Sci., 59, 692–713, 2002.

Mickley, L. J., Jacob, D. J., Field, B. D., and Rind, D.:Effects of future climate change on regional air pollutionepisodes in the United States, Geophys. Res. Let., 31, L24103,doi:10.1029/2004GL021216, 2004.

Mickley, L. J., Murti, P. P., Jacob, D. J., Logan, J. A., Koch, D. M.,and Rind, D.: Radiative forcing from tropospheric ozone calcu-lated with a unified chemistry-climate model, J. Geophys. Res.,104(D23), 30 153–30 172, doi:10.1029/1999JD900439, 1999.

Mishchenko, M. I., Rossow, W. B., Macke, A., and Lacis, A. A.:Sensitivity of cirrus cloud albedo, bidirectional reflectance andoptical thickness retrieval accuracy to ice particle shape, J. Geo-phys. Res., 101(D12), 16 973–16 986, doi:10.1029/96JD01155,1996.

Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., andClough, S. A.: Radiative transfer for inhomogeneous atmo-sphere: RRTM, a validated correlated-k model for the longwave,J. Geophys. Res., 102(D14), 16 663–16 682, 1997.

Mahowald, N. M., Lamarque, J.-F., Tie, X. X., and Wolff, E.: Sea-salt aerosol response to climate change: Last Glacial Maximum,preindustrial, and doubled carbon dioxide climates, J. Geophys.Res., 111, D05303, doi:10.1029/2005JD006459, 2006a.

Mahowald N. M., Muhs, D. R., Levis, S., Rasch, P. J., Yoshioka, M.,Zender, C. S., Luo, C.: Change in atmospheric mineral aerosolsin response to climate: Last glacial period, preindustrial, mod-ern, and doubled carbon dioxide climates, J. Geophys. Res., 111,D10202, doi:10.1029/2005JD006653, 2006b.

Nagashima, T., Takahashi, M., Takigawa, M., and Akiyoshi, H.:Future development of the ozone layer calculated by a generalcirculation model with fully interactive chemistry, Geophys. Res.Lett., 29(8), 1162, doi:10.1029/2001GL014026, 2002.

Napari, I., Noppel, M., Vehkamaki, H., and Kulmala, M.: Param-eterization of ternary nucleation rates for H2SO4-NH3-H2O va-pors, J. Geophys. Res., 107, 4381, doi:10.1029/2002JD002132,2002.

Neelin, J. D. and Zeng, N.: The first quasi-equilibrium tropical cir-culation model—Formulation, J. Atmos. Sci., 57, 1741–1766,2000.

Nenes, A., Pandis, S. N., and Pilinis, C.: ISORROPIA: A new ther-modynamic equilibrium model for multiphase multicomponentinorganic aerosols, Aquat. Geochem., 4, 123–152, 1998.

Ng, N. L., Kroll, J. H., Chan, A. W. H., Chhabra, P. S., Flagan,R. C., and Seinfeld, J. H.: Secondary organic aerosol formationfrom m-xylene, toluene, and benzene, Atmos. Chem. Phys., 7,3909-3922, 2007,http://www.atmos-chem-phys.net/7/3909/2007/.

Ng, N. L., Chhabra, P. S., Chan, A. W. H., Surratt, J. D., Kroll, J.H., Kwan, A. J., McCabe, D. C., Wennberg, P. O., Sorooshian,A., Murphy, S. M., Dalleska, N. F., Flagan, R. C., and Seinfeld,J. H.: Effect of NOx level on secondary organic aerosol (SOA)formation from the photooxidation of terpenes, Atmos. Chem.Phys., 7, 5159-5174, 2007,http://www.atmos-chem-phys.net/7/5159/2007/.

Noppel, M., Vehkamaki, H., and Kulmala, M.: An improved modelfor hydrate formation in sulfuric acid-water nucleation, J. Chem.Phys., 116, 218–228, 2002.

Odum, J. R., Jungkamp, T. P. W., Griffin, R. J., Forstner, H. J. L.,

Flagan, R. C., and Seinfeld, J. H.: Aromatics, reformulated gaso-line, and atmospheric organic aerosol formation, Environ. Sci.Technol., 31, 1890–1897, 1997.

O’Connor, F., Mann, G., Morgenstern, O., Carslaw, K., Johnson,C., Pyle, J., Bellouin, N., Bushell, A., Carver, G., Osprey , S.,Rae, J., Sanderson, M., and Young, P.: Report on chemistryand aerosols modeling in UKCA, UACK project, May 2006,http://www.ukca.ac.uk, 2006.

Pagowski, M., Grell, G. A., Devenyi, D., Peckham, S. E., McKeen,S. A., Gong, W., Delle Monache, L., McHenry, J. H., McQueen,J., and Lee, P.: Application of dynamic linear regression to im-prove the skill of ensemble-based deterministic ozone forecasts,Atmos. Environ., 40, 3240–3250, 2006.

Pan, Y., Hu, X.-M., and Zhang, Y.: Sensitivity of gaseous andaerosol predictions to gas-phase chemical mechanisms, presen-tation at the 10th Conference on Atmospheric Chemistry, NewOrleans, Louisiana, 20-24 January, 2008.

Pandis, S. N., Harley, R. A., Cass, G. R., and Seinfeld, J. H.: Sec-ondary organic aerosol formation and transport, Atmos. Envi-ron., 26A, 2269–2282, 1992.

Penenko, V. V., Aloyan, A. E., Bazhin, N. M., and Skubnevskaya,G. I.: Chislennaya Model’ Gidrometeorologicheskogo RezhimaI Zagryazneniya Atmosfery Gorodov I Promyshlennykh Ray-onoa (A Mathematical Model of Hydrometeorological Condi-tions and Pollution of The Atmospheric of Cities and IndustrialRegions), Meteorologiya i Gidroloqiya, Moscow, 4, 5-15, 1984.

Penenko, V. V. and Aloyan, A. E.: Modeli I Metody dlya ZadachOkhrany Okruzhayushchei Sredy (Models and methods for envi-ronment protection problems), Nauka, Novosibirsk, 256 pp. (inRussian), 1985.

Pankow, J. F.: An absorption model of gas-particle partitioningof organic compounds in the atmosphere, Atmos. Environ., 28,185–188, 1994a.

Pankow, J. F.: An absorption model of the gas/aerosol partitioninginvolved in the formation of secondary organic aerosol, Atmos.Environ., 28, 189–193, 1994b.

Penner, J. E.: Comment on “Control of fossil&hyphen;fuel particu-late black carbon and organic matter, possibly the most effectivemethod of slowing global warming” by Jacobson, M. Z., J. Geo-phys. Res., 108(D24), 4771, doi:10.1029/2002JD003364, 2003.

Penner, J. E., Zhang, S. Y., and Chuang, C. C.: Soot and smokeaerosol may not warm climate, J. Geophys. Res., 108(D21),4657, doi:10.1029/2003JD003409, 2003.

Pitari, G., Palermi, S., Visconti, G., and Prinn, R. G.: Ozone Re-sponse to a CO2 Doubling: Results From a stratospheric cir-culation model with heterogeneous chemistry, J. Geophys. Res.,97(D5), 5953–5962, doi:10.1029/92JD00164, 1992.

Pitari, G., Mancini, E., Rizi, V., and Shindell, D.: Impact of fu-ture climates and emission changes on stratospheric aerosol andclimate, J. Atmos. Sci., 59, 414–440, 2002.

Posfai, M., Anderson, J. R., Buseck, P. R., and Sievering, H.: Sootand sulfate aerosol particles in the remote marine troposphere, J.Geophys. Res., 104, 21 685–21 693, 1999.

Prather, M., Gauss, M., Berntsen, T., Isaksen, I., Sundet, J., Bey, I.,Brasseur, G., Dentener, F., Derwent, R., Stevenson, D., Gren-fell, L., Hauglustaine, D., Horowitz, L., Jacob, D., Mickley,L., Lawrence, M., von Kuhlmann, R., Muller, J.-F., Pitari, G.,Rogers, H., Johnson, M., Pyle, J., Law, K., van Weele, M., andWild, O.: Fresh air in the 21st century?, Geophys. Res. Lett.,




http://www.ukca.ac.uk


30(2), 1100, doi:10.1029/2002GL016285, 2003.Prinn, R., Jacoby, H., Sokolov, A., Wang, C., Xiao, X., Yang, Z.,

Eckhaus, R., Stone, P., Ellerman, D., Melillo, J., Fitzmaurice, J.,Kicklighter, D., Holian, G., and Liu, Y.: Integrated global systemmodel for climate policy assessment feedbacks and sensitivitystudies, Climatic Change, 41, 469–546, 1999.

Pun, B.: Development and initial application of the sesqui-versionof MADRID, J. Geophys. Res., in press, 2008.

Pun, B. K., Griffin, R. J., Seigneur, C., and Seinfeld, J. H.:Secondary organic aerosol: 2. Thermodynamic model forgas/particle partitioning of molecular constituents, J. Geophys.Res., 107(D17), 4333, doi:10.1029/2001JD000542, 2002.

Pun, B., Seigneur, C., and Lohman, K.: Modeling secondary or-ganic aerosol formation via multiphase partitioning with molec-ular data, Environ. Sci. Technol., 40, 4722–4731, 2006.

Qian, Y. and Giorgi, F.: Interactive coupling of regional climateand sulfate aerosol models over eastern Asia, J. Geophys. Res.,104(D6), 6477–6500, doi:10.1029/98JD02347, 1999.

Qian, Y., Giorgi, F., Huang, Y., Chameides, W., and Luo, C.: Re-gional simulation of anthropogenic sulfur over East Asia and itssensitivity to model parameters, Tellus, 53B, 171–191, 2001.

Rasch, P. J. and Kristjansson, J. E.: A comparison of the CCM3model climate using diagnosed and predicted condensate param-eterizations. J. Climate, 11, 1587-1614, 1998.

Rasch, P. J., Barth, M. C., Kiehl, J. T., Schwartz, S. E., andBenkovitz, C. M.: A description of the global sulfur cycle and itscontrolling processes in the National Center for Atmospheric Re-search Community Climate Model Version 3, J. Geophys. Res.,105, 1367–1385, 2000a.

Rasch, P. J., Boville, B. A., and Brasseur, G. P.: A three-dimensional general circulation model with coupled chemistryfor the middle atmosphere, J. Geophys. Res., 100(D5), 9041–9072, doi:10.1029/95JD00019, 1995.

Rasch, P. J., Coleman, D. B., Mahowald, N., Williamson, D. L., Lin,S.-J., Boville, B. A., and Hess, P.: Characteristics of atmospherictransport using three numerical formulations for atmospheric dy-namics in a single GCM framework, J. Climate, 19, 2243–2266,2006a.

Rasch, P. J., Stevens, M. J., Ricciardulli, L., Dai, A., Negri, A.,Wood, R., Boville, B. A., Eaton, B., and Hack, J. J.: Charac-terization of tropical transient activity in the CAM3 atmospherichydrologic cycle, J. Climate, 19, 2222–2242, 2006b.

Roelofs, G.-J. and Lelieveld, J.: Distribution and budget of O3 inthe troposphere calculated with a chemistry general circulationmodel, J. Geophys. Res., 100(D10), 20 983–20 998, 1995.

Roelofs, G.-J., Lelieveld, J., and Ganzeveld, L.: Simulation ofglobal sulfate distribution and the influence of effective clouddrop radii with a coupled photochemistry-sulfur cycle model,Tellus, Ser. B, 50, 224–242, 1998.

Rose, K. and Brasseur, G.: A three-dimensional model ofchemically active trace species in the middle atmosphere dur-ing disturbed winter conditions, J. Geophys. Res., 94(D13),1638716403, doi:10.1029/89JD01092, 1989.

Rosenfeld, D.: TRMM Observed First Direct Evidence of Smokefrom Forest Fires Inhibiting Rainfall, Geophys. Res. Lett.,26(20), 3105–3108, 1999.

Rosenfeld, D.: Suppression of Rain and Snow by Urban and Indus-trial Air Pollution, Science, 287(5459), 1793–1796, 2000.

Rosenfeld, D. and Lensky, I. M.: Spaceboren sensed insights into

precipitation formation processes in continental and maritimeclouds, B. Am. Meteorol. Soc., 79, 2457–2476, 1998.

Rosenfeld, D. and Woodley, W. L.: Satellite-inferred impact ofaerosols on the microstructure of Thai convective clouds. Pro-cessings, Seven WMO Scientific Conference on Weather Mod-ification, Chiang Mai, Thailand, 17–22 February 1999, 17–20,1999.

Rozanov, E. V., Schlesinger, M. E., and Zubov, V. A.: TheUniversity of Illinois, Urbana-Champaign three-dimensionalstratosphere-troposphere general circulation model with in-teractive ozone photochemistry: Fifteen-year control runclimatology, J. Geophys. Res., 106(D21), 2723327254,doi:10.1029/2000JD000058, 2001.

Sanderson, M. G., Collins, W. J., Johnson, C. E., and Derwent, R.G.: Present and future acid deposition to ecosystems: The effectof climate change, Atmos. Environ., 40, 1275–1283, 2006.

Sarwar, G., Luecken, D., Yarwood, G., Whitten, G. Z., and Carter,W. P. L.: Implementing an updated carbon bond mechanism intothe community multiscale air quality model, presentation at the2005 Models-3 Workshop, 26–28 September, Chapel Hill, NC.,2005.

Sarwar, G., Luecken, D., Yarwood, G., Whitten, G. Z., and Carter,W. P. L.: Impact of an updated carbon bond mechanism on pre-dictions from the community multiscale air quality model, J.Appl. Meteorol., in press, 2008.

Schell, B., Ackermann, I. J., Hass, H., Binkowski, F. S., and Ebel,A.: Modeling the formation of secondary organic aerosol withina comprehensive air quality model system, J. Geophys. Res., 106,28 275–28 293, 2001.

Schlesimger, M. E. and Mintz, Y.: Numerical simulation of ozoneproduction, transport and distribution with a global atmosphericgeneral circulation model, J. Atmos. Sci., 36, 1325–1361, 1979.

Schnadt, C., Dameris, M., Ponater, M., Hein, R., Grewe, V., andSteil, B.: Interaction of atmospheric chemistry and climate andits impact on stratospheric ozone, Clim. Dynam., 18, 501–517,2002.

Seaman, N. L.: Meteorological modeling for air-quality assess-ments, Atmos. Environ., 34, 2231–2259, 2000.

Seigneur, C. S.: Air pollution: current challenges and future oppor-tunities, AIChE Journal, February, 51(2), 356–364, 2005.

Seinfeld, J. H.: Air Pollution: A Half Century of Progress, AIChEJournal, June, 50(6), 1096–1108, 2004.

Shindell, D. T., Rind, D., and Lonergan, P.: Increased polar strato-spheric ozone losses and delayed eventual recovery owing to in-creasing greenhouse-gas concentrations, Nature, 392, 589–592,doi:10.1038/33385, 1998.

Shindell, D. T., Lee Grenfell, J., Rind, D., Grewe, V., and Price,C.: Chemistry-climate interactions in the Goddard Institutefor Space Studies general circulation model, 1, Troposphericchemistry model description and evaluation, J. Geophys. Res.,106(D8), 8047–8076, doi:10.1029/2000JD900704, 2001.

Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O.,Barker, D. M., Wang, W., and Powers, J. G.: A descrip-tion of the advanced research WRF version 2, NCAR Tech-nical Note, NCAR/TN468+STR, 88 pp., http://wrf-model.org/wrfadmin/publications.php, 2005.

Sofiev, M.: Aerosol species in the AQ forecasting system of FMI:possibilities for coupling with NWP models, presentation at theCOST-728/NetFAM workshop on Integrated systems of meso-


http://wrf-model.org/wrfadmin/publications.php

http://wrf-model.org/wrfadmin/publications.php


meteorological and chemical transport models, Copenhagen,Denmark, 21–23 May, 2007.

Steil, B., Bruhl, C., Manzini, E., Crutzen, P. J., Lelieveld, J.,Rasch, P. J., Roeckner, E., and Kruger, K.: A new interactivechemistry-climate model: 1. Present-day climatology and inter-annual variability of the middle atmosphere using the model and9 years of HALOE/UARS data, J. Geophys. Res., 108(D9), 4290,doi:10.1029/2002JD002971, 2003.

Stier, P., Feichter, J., Kinne, S., Kloster, S., Vignati, E.,Wilson, J.,Ganzeveld, L., Tegen, I., Werner, M., Balkanski, Y., Schulz,M., Boucher, O., Minikin, A., and Petzold, A.: The aerosol-climate model ECHAM5-HAM, Atmos. Chem. Phys., 5, 1125-1156, 2005,http://www.atmos-chem-phys.net/5/1125/2005/.

Stier, P., Seinfeld, J. H., Kinne, S., and Boucher O.: Aerosol absorp-tion and radiative forcing, Atmos. Chem. Phys., 7, 5237-5261,2007

Stockwell, W. R., Kirchner, F., Kuhn, M., and Seefeld, S.: A newmechanism for regional atmispheric chemistry modeling, J. Geo-phys. Res., 102, 25 847–25 879, 1997.

Stockwell, W. R., Middleton, P., Chang, J. S., and Tang, X.:The second generation regional acid deposition model chemicalmechanism for regional air quality modeling, J. Geophys. Res.,95, 16 343–16 367, 1990.

Stokes, R. H. and Robinson, R. A.: Interactions in aqueous nonelec-trolyte solutions, I, Solute-solvent equilibria, J. Phys. Chem., 70,2126–2130, 1966.

Taghavi, M., Cautenet, S., and Foret, G.: Simulation of ozone pro-duction in a complex circulation region using nested grids, Atmo.Chem. Phys., 4, 825–838, 2004.

Takigawa, M., Takahashi, M., and Akiyoshi, H.: Simulation ofozone and other chemical species using a Center for ClimateSystems Research/National Institute for Environmental Studiesatmospheric GCM with coupled stratospheric chemistry, J. Geo-phys. Res., 104, 14 003–14 018, 1999.

Taylor, K. E. and Penner, J. E.: Response of the climate system toatmospheric aerosols and greenhouse gases, Nature, 369, 734–737, doi:10.1038/369734a0, 1994.

Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer,S., Berntsen, T., Berglen, T., Boucher, O., Chin, M., Dentener, F.,Diehl, T., Easter, R., Feichter, H., Fillmore, D., Ghan, S., Ginoux,P., Gong, S., Grini, A., Hendricks, J., Horrowitz, L., Isaksen, I.,Iversen, T., Kirkevag, A., Kloster, S., Koch, D., Kristjansson, J.E., Krol, M., Lauer, A., Lamarque, J. F., Liu, X., Montanaro,V., Myhre, G., Penner,J., Pitari, G., Reddy, S., Seland, O., Stier,P., Takemura, T., and Tie, X.: Analysis and quantification of thediversities of aerosol life cycles within AeroCom, Atm. Chem.Phy., 6, 1777–1813, 2006.

Thompson, S. L.: Global interactive transport simulations of nu-clear war smoke, Nature, 317, 35–39, 1985.

Tie, X., Madronich, S., Walters, S., Edwards,D. P., Ginoux, P., Ma-howald, N., Zhang, R., Lou, C., and Brasseur, G.: Assessment ofthe global impact of aerosols on tropospheric oxidants, J. Geo-phys. Res., 110, D03204, doi:10.1029/2004JD005359, 2005.

Tie, X.-X., Madronich, S., Li, G.-H., Ying, Z.-M., Zhang, R., Gar-cia, A. R., Lee-Taylor, J., and Liu, Y.-B.: Characterizations ofchemical oxidants in Mexico City: A regional chemical dynami-cal model (WRF/Chem) study, Atmos. Environ., 41, 1989-2008,2007.

Tulet, P., Crassier, V., Cousin, F., Suhre, K., and Rosset,R.: ORILAM, a three-moment lognormal aerosol scheme formesoscale atmospheric model: Online coupling into the Meso-NH-C model and validation on the escompte campaign, J. Geo-phys. Res., 110, D18201, doi:10.1029/2004JD005716, 2005.

Tulet P., Crassier, V., Solmon, F., Guedalia, D., and Rosset, R.: De-scription of the Mesoscale Non Hydrostatic Chemistry modeland application to a transboundary pollution episode betweennorthern France and southern England, J. Geophys. Res., 108,4021, doi.10.1029/2000JD000301, 2003.

Tulet, P., Grini, A., Griffin, R. J., and Petitcol, S.: ORILAM-SOA:A computationally efficient model for predicting secondary or-ganic aerosols in three-dimensional atmospheric models, J. Geo-phys. Res., 111, D23208, doi:10.1029/2006JD007152, 2006.

Uno, I., Amano, H., Emori, S., Kinoshita, K., Matsui, I., and Sugi-moto, N.: Trans-Pacific yellow sand transport observed in April1998: A numerical simulation, J. Geophys. Res., 106(D16),18 331–18 344, doi:10.1029/2000JD900748, 2001.

Uno I., Carmichael, G. R., Streets, D. G., Tang, Y., Yienger, J.J., Satake, S., Wang, Z., Woo, J.-H., Guttikunda, S., Uematsu,M., Matsumoto, K., Tanimoto, H., Yoshioka, K., and Iida, T.:Regional chemical weather forecasting system CFORS: Modeldescriptions and analysis of surface observations at Japanese is-land stations during the ACE-Asia experiment, J. Geophys. Res.,108(D23), 8668, doi:10.1029/2002JD002845, 2003.

Vehkamaki, H., Kulmala, M., Napari, I., Lehtinen, K. E. J., Timm-reck, C., Noppel, M., and Laaksonen, A.: An improved pa-rameterization for sulfuric acid-water nucleation rates for tro-pospheric and stratospheric conditions, J. Geophys. Res., 107,4622, doi:10.1029/2002JD002184, 2002.

Vogel, B., Hoose, C., Vogel, H., and Kottmeier, Ch.: A model ofdust transport applied to the Dead Sea area, Meteorol. Z., 14,611–624, 2006.

Vogel, H.: LM-ART-aerosols and reactive trace gases within LM,presentation at the COST728/NetFAM workshop on Integratedsystems of meso-meteorological and chemical transport models,Copenhagen, Denmark, 21–23 May, 2007.

Warner, J.: A reduction in rainfall associated with smoke from sugarcane fires – an inadvertent weather modification?, J. Appl. Me-teor., 7, 247–251, 1968.

Washington, W. M.: Documentation for the Community ClimateModel (CCM), Version 0. Tech. Rep., National Center for Atmo-spheric Research, Boulder, CO, 222pp., 1982.

Wexler, A. S., Lurmann, F. W., and Seinfeld, J. H.: Modeling Urbanand Regional Aerosols, I. Model Development, Atmos. Environ.,28, 531–546, 1994.

Wild, O., Zhu, X., and Prather, M. J.: Fast-J: Accurate simulation ofin- and below cloud photolysis in tropospheric chemical models,J. Atmos. Chem., 37, 245–282, 2000.

Williamson, D. L., Kiehl, J. T., Ramanathan, V., Dickinson, R.E., and Hack, J. J.: Description of NCAR Community ClimateModel (CCM1). Tech. Rep. NCAR/TN-285STR, National Cen-ter for Atmospheric Research, Boulder, CO, 112 pp., 1987.

Wolke, R., Hellmuth, O., Knoth, O., Schroder, W., Heinrich, B.,and Renner, E.: The chemistry-transport modeling system LM-MUSCAT: Description and CITYDELTA applications. Proceed-ings of the 26th International Technical Meeting on Air Pollutionand Its Application, Istanbul, May 2003, 369–379, 2003.

Wong, S., Wang, W.-C., Isaksen, I. S. A., Berntsen, T. K., and Sun-




det, J. K.: A global climate-chemistry model study of present-day tropospheric chemistry and radiative forcing from changes intropospheric O3 since the reindustrial period, J. Geophys. Res.,109, D11309, doi:10.1029/2003JD003998, 2004.

Xiu, A., Mathur, R., and Coats, C.: On the development and testingof an air quality modeling system with integrated meteorology,chemistry, and emissions, Proceedings, Symposium on Interdis-ciplinary Issues in Atmospheric Chemistry, Dallas, TX, Ameri-can Meteorology Society, 117–120, 1999.

Yarwood, G., Rao, S., and Yocke, M.: Updates to the carbon bondchemical mechanism: CB05, Final Report prepared for the U.S.Environmental Protection Agency, RT-04-00675, ENVIRON In-ternational Corporation, Novato, CA 94945, 2005.

Zaveri, R. A.: Development and evaluation of a comprehensive tro-pospheric chemistry model for regional and global applications,Ph.D. thesis, Va. Polytech. Inst. and State Univ., Blacksburg, Va.,1997.

Zaveri, R. A. and Peters, L. K.: A new lumped structure photochem-ical mechanism for large-scale applications, J. Geophys. Res.,104, 30 387–30 415, 1999.

Zaveri, R. A., Easter, R. C., and Peters, L. K.: Acomputationally efficient Multicomponent Equilibrium Solverfor Aerosols (MESA), J. Geophys. Res., 110, D24203,doi:10.1029/2004JD005618, 2005a.

Zaveri, R. A., Easter, R. C., and Wexler, A. S.: A new methodfor multicomponent activity coefficients of electrolytes in aque-ous atmospheric aerosols, J. Geophys. Res., 110, D02201,doi:10.1029/2004JD004681, 2005b.

Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Modelfor Simulating Aerosol Interactions and Chemistry (MOSAIC),J. Geophys. Res., doi:10.1029/2007jd008782, in press, 2008, inpress, 2008.

Zdanovskii, A. B.: New methods of calculating solubilities of elec-trolytes in multicomponent systems, Z. Fiz. Khim, 22, 1475–1485, 1948.

Zhang, Y., Pun, B., Vijayaraghavan, K., Wu, S.-Y., Seigneur, C.,Pandis, S., Jacobson, M., Nenes, A., and Seinfeld, J. H.: Devel-opment and application of the model for aerosol dynamics, re-action, ionization and dissolution (MADRID), J. Geophys. Res.,109, D01202, doi:10.1029/2003JD003501, 2004.

Zhang, Y., Seigneur, C., Seinfeld, J. H., Jacobson, M. Z., andBinkowski, F. S.: Simulation of aerosol dynamics: A compar-ative review of algorithms used in air quality models, AerosolSci. Technol., 31, 487–514, 1999.

Zhang, Y., Seigneur, C., Seinfeld, J. H., Jacobson, M., Clegg,

S. L., and Binkowski, F. S.: A Comparative Review of Inor-ganic Aerosol Thermodynamic Equilibrium Modules: Similar-ities, Differences, and Their Likely Causes, Atmos. Environ., 34,117–137, 2000.

Zhang, Y., Easter, R. C., Ghan, S. J., and Abdul-Razzak, H.: Impactof aerosol size representation on modeling aerosol-cloud interac-tions, J. Geophys. Res., 107, 4558, doi:10.1029/2001JD001549,2002.

Zhang, Y., Hu, X.-M., Howell, G. W., Sills, E., Fast, J. D.,Gustafson Jr., W. I., Zaveri, R. A., Grell, G. A., Peckham, S.E., and McKeen, S. A.: Modeling Atmospheric Aerosols inWRF/CHEM, oral presentation at the 2005 Joint WRF/MM5User’s Workshop, 27–30 June, Boulder, CO, 2005a.

Zhang, Y., Hu, X.-M., Wang, K., Huang, J.-P., Fast, J. D., GustafsonJr., W. I., Chu, D. A., and Jang, C.: Evaluation of WRF/Chem-MADRID with Satellite and Surface Measurements: Chemicaland Optical Properties of Aerosols, oral presentation at the 2005AGU Fall Meeting, 5–9 December 2005, San Francisco, CA,USA, 2005c.

Zhang, Y. and Jacobson, M.Z.: Implementation and Testing of EQ-UISOLV II in the CMAQ Modeling System, oral presentationat the 2005 Models-3 Workshop, 26–28 September, Chapel Hill,NC, 2005a.

Zhang, Y. and Jacobson, M.Z.: A Comparative Study of NucleationParameterizations for 3-D Atmospheric Models, oral presenta-tion at the 9th Atmospheric Sciences and Air Quality Conference(ASAAQ2005), 27–29 April, San Francisco, CA, 2005b.

Zhang, Y., Hu, X.-M., Wen, X.-Y., Schere, K. L., and Jang, C. J.:Simulating Climate-Chemistry-Aerosol-Cloud-Radiation Feed-backs in WRF/Chem: Model Development and Initial Applica-tion, oral presentation at the 6th Annual CMAS Models-3 User’sConference, 1–3 October 2007, Chapel Hill, NC, 2007a.

Zhang, Y., Huang, J.-P., Henze, D. K., and Seinfeld, J.H.: Role of isoprene in secondary organic aerosol forma-tion on a regional scale, J. Geophys. Res., 112, D20207,doi:10.1029/2007JD008675, 2007b.

Zhang, Y., Wen, X.-Y., Hu, X.-M., and Jang, C. J.: Simulating theEffect of Aerosols on Regional Climate Using WRF/Chem, oralpresentation at the 10th Conference on Atmospheric Chemistry,New Orleans, Louisiana, 20–24 January, 2008a.

Zhang, Y., Wen, X.-Y., Skamarock, W. C., Grell, G. A., Peckham, S.E., Ghan, S. J., and Easter, R. C.: Some Preliminary Results fromthe Development of Global WRF/Chem, oral presented the 10thConference on Atmospheric Chemistry, New Orleans, Louisiana,20–24 January, 2008b.


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